A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
A set of genes descended by duplication and variation from some ancestral gene. Such genes may be clustered together on the same chromosome or dispersed on different chromosomes. Examples of multigene families include those that encode the hemoglobins, immunoglobulins, histocompatibility antigens, actins, tubulins, keratins, collagens, heat shock proteins, salivary glue proteins, chorion proteins, cuticle proteins, yolk proteins, and phaseolins, as well as histones, ribosomal RNA, and transfer RNA genes. The latter three are examples of reiterated genes, where hundreds of identical genes are present in a tandem array. (King & Stanfield, A Dictionary of Genetics, 4th ed)
The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.
Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.
The relationships of groups of organisms as reflected by their genetic makeup.
Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.
Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.
Technique that utilizes low-stringency polymerase chain reaction (PCR) amplification with single primers of arbitrary sequence to generate strain-specific arrays of anonymous DNA fragments. RAPD technique may be used to determine taxonomic identity, assess kinship relationships, analyze mixed genome samples, and create specific probes.
A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.
Genotypic differences observed among individuals in a population.
A statistically significant excess of cases of a disease, occurring within a limited space-time continuum.
A technique for identifying individuals of a species that is based on the uniqueness of their DNA sequence. Uniqueness is determined by identifying which combination of allelic variations occur in the individual at a statistically relevant number of different loci. In forensic studies, RESTRICTION FRAGMENT LENGTH POLYMORPHISM of multiple, highly polymorphic VNTR LOCI or MICROSATELLITE REPEAT loci are analyzed. The number of loci used for the profile depends on the ALLELE FREQUENCY in the population.
A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Deoxyribonucleic acid that makes up the genetic material of bacteria.
Procedures for identifying types and strains of bacteria. The most frequently employed typing systems are BACTERIOPHAGE TYPING and SEROTYPING as well as bacteriocin typing and biotyping.
A primary headache disorder that is characterized by severe, strictly unilateral PAIN which is orbital, supraorbital, temporal or in any combination of these sites, lasting 15-180 min. occurring 1 to 8 times a day. The attacks are associated with one or more of the following, all of which are ipsilateral: conjunctival injection, lacrimation, nasal congestion, rhinorrhea, facial SWEATING, eyelid EDEMA, and miosis. (International Classification of Headache Disorders, 2nd ed. Cephalalgia 2004: suppl 1)
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.
The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.
A group of proteins possessing only the iron-sulfur complex as the prosthetic group. These proteins participate in all major pathways of electron transport: photosynthesis, respiration, hydroxylation and bacterial hydrogen and nitrogen fixation.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.
The science dealing with the earth and its life, especially the description of land, sea, and air and the distribution of plant and animal life, including humanity and human industries with reference to the mutual relations of these elements. (From Webster, 3d ed)
The detection of RESTRICTION FRAGMENT LENGTH POLYMORPHISMS by selective PCR amplification of restriction fragments derived from genomic DNA followed by electrophoretic analysis of the amplified restriction fragments.
In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.
The functional hereditary units of BACTERIA.
The restriction of a characteristic behavior, anatomical structure or physical system, such as immune response; metabolic response, or gene or gene variant to the members of one species. It refers to that property which differentiates one species from another but it is also used for phylogenetic levels higher or lower than the species.
Constituent of 30S subunit prokaryotic ribosomes containing 1600 nucleotides and 21 proteins. 16S rRNA is involved in initiation of polypeptide synthesis.
Sequential operating programs and data which instruct the functioning of a digital computer.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Proteins found in any species of bacterium.
Variation occurring within a species in the presence or length of DNA fragment generated by a specific endonuclease at a specific site in the genome. Such variations are generated by mutations that create or abolish recognition sites for these enzymes or change the length of the fragment.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.
The systematic arrangement of entities in any field into categories classes based on common characteristics such as properties, morphology, subject matter, etc.
In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)
Deep grooves or clefts in the surface of teeth equivalent to class 1 cavities in Black's classification of dental caries.
A variety of simple repeat sequences that are distributed throughout the GENOME. They are characterized by a short repeat unit of 2-8 basepairs that is repeated up to 100 times. They are also known as short tandem repeats (STRs).
A set of statistical methods for analyzing the correlations among several variables in order to estimate the number of fundamental dimensions that underlie the observed data and to describe and measure those dimensions. It is used frequently in the development of scoring systems for rating scales and questionnaires.
Elements of limited time intervals, contributing to particular results or situations.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
The application of molecular biology to the answering of epidemiological questions. The examination of patterns of changes in DNA to implicate particular carcinogens and the use of molecular markers to predict which individuals are at highest risk for a disease are common examples.
Techniques which study entities using their topological, geometric, or geographic properties and include the dimension of time in the analysis.
The degree of similarity between sequences of amino acids. This information is useful for the analyzing genetic relatedness of proteins and species.
Deoxyribonucleic acid that makes up the genetic material of plants.
Gel electrophoresis in which the direction of the electric field is changed periodically. This technique is similar to other electrophoretic methods normally used to separate double-stranded DNA molecules ranging in size up to tens of thousands of base-pairs. However, by alternating the electric field direction one is able to separate DNA molecules up to several million base-pairs in length.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
The process of cumulative change at the level of DNA; RNA; and PROTEINS, over successive generations.
Partial cDNA (DNA, COMPLEMENTARY) sequences that are unique to the cDNAs from which they were derived.
Short sequences (generally about 10 base pairs) of DNA that are complementary to sequences of messenger RNA and allow reverse transcriptases to start copying the adjacent sequences of mRNA. Primers are used extensively in genetic and molecular biology techniques.
DNA sequences encoding RIBOSOMAL RNA and the segments of DNA separating the individual ribosomal RNA genes, referred to as RIBOSOMAL SPACER DNA.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Electrophoresis in which a starch gel (a mixture of amylose and amylopectin) is used as the diffusion medium.
Geographic variety, population, or race, within a species, that is genetically adapted to a particular habitat. An ecotype typically exhibits phenotypic differences but is capable of interbreeding with other ecotypes.
The biological objects that contain genetic information and that are involved in transmitting genetically encoded traits from one organism to another.
Computer-based representation of physical systems and phenomena such as chemical processes.
A country spanning from central Asia to the Pacific Ocean.
The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
The insertion of recombinant DNA molecules from prokaryotic and/or eukaryotic sources into a replicating vehicle, such as a plasmid or virus vector, and the introduction of the resultant hybrid molecules into recipient cells without altering the viability of those cells.
The systematic surveying, mapping, charting, and description of specific geographical sites, with reference to the physical features that were presumed to influence health and disease. Medical topography should be differentiated from EPIDEMIOLOGY in that the former emphasizes geography whereas the latter emphasizes disease outbreaks.
Tandem arrays of moderately repetitive, short (10-60 bases) DNA sequences which are found dispersed throughout the GENOME, at the ends of chromosomes (TELOMERES), and clustered near telomeres. Their degree of repetition is two to several hundred at each locus. Loci number in the thousands but each locus shows a distinctive repeat unit.
One of the three domains of life (the others being Eukarya and ARCHAEA), also called Eubacteria. They are unicellular prokaryotic microorganisms which generally possess rigid cell walls, multiply by cell division, and exhibit three principal forms: round or coccal, rodlike or bacillary, and spiral or spirochetal. Bacteria can be classified by their response to OXYGEN: aerobic, anaerobic, or facultatively anaerobic; by the mode by which they obtain their energy: chemotrophy (via chemical reaction) or PHOTOTROPHY (via light reaction); for chemotrophs by their source of chemical energy: CHEMOLITHOTROPHY (from inorganic compounds) or chemoorganotrophy (from organic compounds); and by their source for CARBON; NITROGEN; etc.; HETEROTROPHY (from organic sources) or AUTOTROPHY (from CARBON DIOXIDE). They can also be classified by whether or not they stain (based on the structure of their CELL WALLS) with CRYSTAL VIOLET dye: gram-negative or gram-positive.
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
A species of gram-negative, facultatively anaerobic, rod-shaped bacteria (GRAM-NEGATIVE FACULTATIVELY ANAEROBIC RODS) commonly found in the lower part of the intestine of warm-blooded animals. It is usually nonpathogenic, but some strains are known to produce DIARRHEA and pyogenic infections. Pathogenic strains (virotypes) are classified by their specific pathogenic mechanisms such as toxins (ENTEROTOXIGENIC ESCHERICHIA COLI), etc.
Databases devoted to knowledge about specific genes and gene products.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control (induction or repression) of gene action at the level of transcription or translation.
Computer systems capable of assembling, storing, manipulating, and displaying geographically referenced information, i.e. data identified according to their locations.
Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.
The biosynthesis of RNA carried out on a template of DNA. The biosynthesis of DNA from an RNA template is called REVERSE TRANSCRIPTION.
Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control of gene action in neoplastic tissue.
Neoplasms associated with a proliferation of a single clone of PLASMA CELLS and characterized by the secretion of PARAPROTEINS.
Any method used for determining the location of and relative distances between genes on a chromosome.
Process of determining and distinguishing species of bacteria or viruses based on antigens they share.
A variation of the PCR technique in which cDNA is made from RNA via reverse transcription. The resultant cDNA is then amplified using standard PCR protocols.
The simultaneous analysis, on a microchip, of multiple samples or targets arranged in an array format.
Cognitive and emotional processes encompassing magnification of pain-related stimuli, feelings of helplessness, and a generally pessimistic orientation.
Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations.
Ligand-binding assays that measure protein-protein, protein-small molecule, or protein-nucleic acid interactions using a very large set of capturing molecules, i.e., those attached separately on a solid support, to measure the presence or interaction of target molecules in the sample.
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
The systematic study of the complete complement of proteins (PROTEOME) of organisms.
Acquired or learned food preferences.
The fleshy or dry ripened ovary of a plant, enclosing the seed or seeds.
Ribonucleic acid in bacteria having regulatory and catalytic roles as well as involvement in protein synthesis.
Widely used technique which exploits the ability of complementary sequences in single-stranded DNAs or RNAs to pair with each other to form a double helix. Hybridization can take place between two complimentary DNA sequences, between a single-stranded DNA and a complementary RNA, or between two RNA sequences. The technique is used to detect and isolate specific sequences, measure homology, or define other characteristics of one or both strands. (Kendrew, Encyclopedia of Molecular Biology, 1994, p503)
The presence of bacteria, viruses, and fungi in the soil. This term is not restricted to pathogenic organisms.
The phenotypic manifestation of a gene or genes by the processes of GENETIC TRANSCRIPTION and GENETIC TRANSLATION.
Using MOLECULAR BIOLOGY techniques, such as DNA SEQUENCE ANALYSIS; PULSED-FIELD GEL ELECTROPHORESIS; and DNA FINGERPRINTING, to identify, classify, and compare organisms and their subtypes.
A large collection of DNA fragments cloned (CLONING, MOLECULAR) from a given organism, tissue, organ, or cell type. It may contain complete genomic sequences (GENOMIC LIBRARY) or complementary DNA sequences, the latter being formed from messenger RNA and lacking intron sequences.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Statistical interpretation and description of a population with reference to distribution, composition, or structure.
A branch of the facial (7th cranial) nerve which passes through the middle ear and continues through the petrotympanic fissure. The chorda tympani nerve carries taste sensation from the anterior two-thirds of the tongue and conveys parasympathetic efferents to the salivary glands.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
The pattern of GENE EXPRESSION at the level of genetic transcription in a specific organism or under specific circumstances in specific cells.
RNA sequences that serve as templates for protein synthesis. Bacterial mRNAs are generally primary transcripts in that they do not require post-transcriptional processing. Eukaryotic mRNA is synthesized in the nucleus and must be exported to the cytoplasm for translation. Most eukaryotic mRNAs have a sequence of polyadenylic acid at the 3' end, referred to as the poly(A) tail. The function of this tail is not known for certain, but it may play a role in the export of mature mRNA from the nucleus as well as in helping stabilize some mRNA molecules by retarding their degradation in the cytoplasm.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)
Regular course of eating and drinking adopted by a person or animal.
The protein complement of an organism coded for by its genome.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
A field of study concerned with the principles and processes governing the geographic distributions of genealogical lineages, especially those within and among closely related species. (Avise, J.C., Phylogeography: The History and Formation of Species. Harvard University Press, 2000)
Histochemical localization of immunoreactive substances using labeled antibodies as reagents.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
The parts of a macromolecule that directly participate in its specific combination with another molecule.
The intergenic DNA segments that are between the ribosomal RNA genes (internal transcribed spacers) and between the tandemly repeated units of rDNA (external transcribed spacers and nontranscribed spacers).
The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.
The performance of dissections with the aid of a microscope.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
Sudden increase in the incidence of a disease. The concept includes EPIDEMICS and PANDEMICS.
Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.
A spectroscopic technique in which a range of wavelengths is presented simultaneously with an interferometer and the spectrum is mathematically derived from the pattern thus obtained.
The systematic study of the complete DNA sequences (GENOME) of organisms.
The variety of all native living organisms and their various forms and interrelationships.
Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.
The functional hereditary units of PLANTS.
The sequential correspondence of nucleotides in one nucleic acid molecule with those of another nucleic acid molecule. Sequence homology is an indication of the genetic relatedness of different organisms and gene function.
Large natural streams of FRESH WATER formed by converging tributaries and which empty into a body of water (lake or ocean).
Any of the processes by which cytoplasmic or intercellular factors influence the differential control of gene action in bacteria.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
The level of protein structure in which regular hydrogen-bond interactions within contiguous stretches of polypeptide chain give rise to alpha helices, beta strands (which align to form beta sheets) or other types of coils. This is the first folding level of protein conformation.
Scales, questionnaires, tests, and other methods used to assess pain severity and duration in patients or experimental animals to aid in diagnosis, therapy, and physiological studies.
A sequence of amino acids in a polypeptide or of nucleotides in DNA or RNA that is similar across multiple species. A known set of conserved sequences is represented by a CONSENSUS SEQUENCE. AMINO ACID MOTIFS are often composed of conserved sequences.
Deoxyribonucleic acid that makes up the genetic material of fungi.
A genus of gram-negative, aerobic, rod-shaped bacteria widely distributed in nature. Some species are pathogenic for humans, animals, and plants.
A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.
Diseases of plants.
The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. PROTEIN STRUCTURE, QUATERNARY describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).
Biological molecules that possess catalytic activity. They may occur naturally or be synthetically created. Enzymes are usually proteins, however CATALYTIC RNA and CATALYTIC DNA molecules have also been identified.
Single-stranded complementary DNA synthesized from an RNA template by the action of RNA-dependent DNA polymerase. cDNA (i.e., complementary DNA, not circular DNA, not C-DNA) is used in a variety of molecular cloning experiments as well as serving as a specific hybridization probe.
Domesticated bovine animals of the genus Bos, usually kept on a farm or ranch and used for the production of meat or dairy products or for heavy labor.
A technique applicable to the wide variety of substances which exhibit paramagnetism because of the magnetic moments of unpaired electrons. The spectra are useful for detection and identification, for determination of electron structure, for study of interactions between molecules, and for measurement of nuclear spins and moments. (From McGraw-Hill Encyclopedia of Science and Technology, 7th edition) Electron nuclear double resonance (ENDOR) spectroscopy is a variant of the technique which can give enhanced resolution. Electron spin resonance analysis can now be used in vivo, including imaging applications such as MAGNETIC RESONANCE IMAGING.
Use of restriction endonucleases to analyze and generate a physical map of genomes, genes, or other segments of DNA.
A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.
Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.
Iron-containing proteins that transfer electrons, usually at a low potential, to flavoproteins; the iron is not present as in heme. (McGraw-Hill Dictionary of Scientific and Technical Terms, 5th ed)
Social and economic factors that characterize the individual or group within the social structure.
A common nonarticular rheumatic syndrome characterized by myalgia and multiple points of focal muscle tenderness to palpation (trigger points). Muscle pain is typically aggravated by inactivity or exposure to cold. This condition is often associated with general symptoms, such as sleep disturbances, fatigue, stiffness, HEADACHES, and occasionally DEPRESSION. There is significant overlap between fibromyalgia and the chronic fatigue syndrome (FATIGUE SYNDROME, CHRONIC). Fibromyalgia may arise as a primary or secondary disease process. It is most frequent in females aged 20 to 50 years. (From Adams et al., Principles of Neurology, 6th ed, p1494-95)
The ability to detect chemicals through gustatory receptors in the mouth, including those on the TONGUE; the PALATE; the PHARYNX; and the EPIGLOTTIS.
The science, art or practice of cultivating soil, producing crops, and raising livestock.

Ringo, Doty, Demeter and Simard, Cerebral Cortex 1994;4:331-343: a proof of the need for the spatial clustering of interneuronal connections to enhance cortical computation. (1/16506)

It has been argued that an important principle driving the organization of the cerebral cortex towards local processing has been the need to decrease time lost to interneuronal conduction delay. In this paper, I show for a simplified model of the cerebral cortex, using analytical means, that if interneuronal conduction time increases proportional to interneuronal distance, then the only way to increase the numbers of synaptic events occurring in a fixed finite time period is to spatially cluster interneuronal connections.  (+info)

Cluster survey evaluation of coverage and risk factors for failure to be immunized during the 1995 National Immunization Days in Egypt. (2/16506)

BACKGROUND: In 1995, Egypt continued to experience endemic wild poliovirus transmission despite achieving high routine immunization coverage with at least three doses of oral poliovirus vaccine (OPV3) and implementing National Immunization Days (NIDs) annually for several years. METHODS: Parents of 4188 children in 3216 households throughout Egypt were surveyed after the second round of the 1995 NIDs. RESULTS: Nationwide, 74% of children are estimated to have received both NID doses, 17% one NID dose, and 9% neither NID dose. Previously unimmunized (47%) or partially immunized (64%) children were less likely to receive two NID doses of OPV than were fully immunized children (76%) (P < 0.001). Other risk factors nationwide for failure to receive NID OPV included distance from residence to nearest NID site >10 minute walk (P < 0.001), not being informed about the NID at least one day in advance (P < 0.001), and residing in a household which does not watch television (P < 0.001). Based on these findings, subsequent NIDs in Egypt were modified to improve coverage, which has resulted in a marked decrease in the incidence of paralytic poliomyelitis in Egypt. CONCLUSIONS: In selected situations, surveys can provide important information that is useful for planning future NIDs.  (+info)

Clusters of Pneumocystis carinii pneumonia: analysis of person-to-person transmission by genotyping. (3/16506)

Genotyping at the internal transcribed spacer (ITS) regions of the nuclear rRNA operon was performed on isolates of P. carinii sp. f. hominis from three clusters of P. carinii pneumonia among eight patients with haematological malignancies and six with HIV infection. Nine different ITS sequence types of P. carinii sp. f. hominis were identified in the samples from the patients with haematological malignancies, suggesting that this cluster of cases of P. carinii pneumonia was unlikely to have resulted from nosocomial transmission. A common ITS sequence type was observed in two of the patients with haematological malignancies who shared a hospital room, and also in two of the patients with HIV infection who had prolonged close contact on the ward. In contrast, different ITS sequence types were detected in samples from an HIV-infected homosexual couple who shared the same household. These data suggest that person-to-person transmission of P. carinii sp. f. hominis may occur from infected to susceptible immunosuppressed patients with close contact within hospital environments. However direct transmission between patients did not account for the majority of cases within the clusters, suggesting that person-to-person transmission of P. carinii sp. f. hominis infection may be a relatively infrequent event and does not constitute the major route of transmission in man.  (+info)

Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques. (4/16506)

The availability of DNA fingerprinting techniques for Mycobacterium tuberculosis has led to attempts to estimate the extent of recent transmission in populations, using the assumption that groups of tuberculosis patients with identical isolates ("clusters") are likely to reflect recently acquired infections. It is never possible to include all cases of tuberculosis in a given population in a study, and the proportion of isolates found to be clustered will depend on the completeness of the sampling. Using stochastic simulation models based on real and hypothetical populations, the authors demonstrate the influence of incomplete sampling on the estimates of clustering obtained. The results show that as the sampling fraction increases, the proportion of isolates identified as clustered also increases and the variance of the estimated proportion clustered decreases. Cluster size is also important: the underestimation of clustering for any given sampling fraction is greater, and the variability in the results obtained is larger, for populations with small clusters than for those with the same number of individuals arranged in large clusters. A considerable amount of caution should be used in interpreting the results of studies on clustering of M. tuberculosis isolates, particularly when sampling fractions are small.  (+info)

Newly recognized focus of La Crosse encephalitis in Tennessee. (5/16506)

La Crosse virus is a mosquito-borne arbovirus that causes encephalitis in children. Only nine cases were reported in Tennessee during the 33-year period from 1964-1996. We investigated a cluster of La Crosse encephalitis cases in eastern Tennessee in 1997. Medical records of all suspected cases of La Crosse virus infection at a pediatric referral hospital were reviewed, and surveillance was enhanced in the region. Previous unreported cases were identified by surveying 20 hospitals in the surrounding 16 counties. Mosquito eggs were collected from five sites. Ten cases of La Crosse encephalitis were serologically confirmed. None of the patients had been discharged from hospitals in the region with diagnosed La Crosse encephalitis in the preceding 5 years. Aedes triseriatus and Aedes albopictus were collected at the case sites; none of the mosquitos had detectable La Crosse virus. This cluster may represent an extension of a recently identified endemic focus of La Crosse virus infection in West Virginia.  (+info)

Hierarchical cluster analysis applied to workers' exposures in fiberglass insulation manufacturing. (6/16506)

The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis.  (+info)

A taxonomy of health networks and systems: bringing order out of chaos. (7/16506)

OBJECTIVE: To use existing theory and data for empirical development of a taxonomy that identifies clusters of organizations sharing common strategic/structural features. DATA SOURCES: Data from the 1994 and 1995 American Hospital Association Annual Surveys, which provide extensive data on hospital involvement in hospital-led health networks and systems. STUDY DESIGN: Theories of organization behavior and industrial organization economics were used to identify three strategic/structural dimensions: differentiation, which refers to the number of different products/services along a healthcare continuum; integration, which refers to mechanisms used to achieve unity of effort across organizational components; and centralization, which relates to the extent to which activities take place at centralized versus dispersed locations. These dimensions were applied to three components of the health service/product continuum: hospital services, physician arrangements, and provider-based insurance activities. DATA EXTRACTION METHODS: We identified 295 health systems and 274 health networks across the United States in 1994, and 297 health systems and 306 health networks in 1995 using AHA data. Empirical measures aggregated individual hospital data to the health network and system level. PRINCIPAL FINDINGS: We identified a reliable, internally valid, and stable four-cluster solution for health networks and a five-cluster solution for health systems. We found that differentiation and centralization were particularly important in distinguishing unique clusters of organizations. High differentiation typically occurred with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration was also important, but we found that health networks and systems typically engaged in both ownership-based and contractual-based integration or they were not integrated at all. CONCLUSIONS: Overall, we were able to classify approximately 70 percent of hospital-led health networks and 90 percent of hospital-led health systems into well-defined organizational clusters. Given the widespread perception that organizational change in healthcare has been chaotic, our research suggests that important and meaningful similarities exist across many evolving organizations. The resulting taxonomy provides a new lexicon for researchers, policymakers, and healthcare executives for characterizing key strategic and structural features of evolving organizations. The taxonomy also provides a framework for future inquiry about the relationships between organizational strategy, structure, and performance, and for assessing policy issues, such as Medicare Provider Sponsored Organizations, antitrust, and insurance regulation.  (+info)

Double blind, cluster randomised trial of low dose supplementation with vitamin A or beta carotene on mortality related to pregnancy in Nepal. The NNIPS-2 Study Group. (8/16506)

OBJECTIVE: To assess the impact on mortality related to pregnancy of supplementing women of reproductive age each week with a recommended dietary allowance of vitamin A, either preformed or as beta carotene. DESIGN: Double blind, cluster randomised, placebo controlled field trial. SETTING: Rural southeast central plains of Nepal (Sarlahi district). SUBJECTS: 44 646 married women, of whom 20 119 became pregnant 22 189 times. INTERVENTION: 270 wards randomised to 3 groups of 90 each for women to receive weekly a single oral supplement of placebo, vitamin A (7000 micrograms retinol equivalents) or beta carotene (42 mg, or 7000 micrograms retinol equivalents) for over 31/2 years. MAIN OUTCOME MEASURES: All cause mortality in women during pregnancy up to 12 weeks post partum (pregnancy related mortality) and mortality during pregnancy to 6 weeks postpartum, excluding deaths apparently related to injury (maternal mortality). RESULTS: Mortality related to pregnancy in the placebo, vitamin A, and beta carotene groups was 704, 426, and 361 deaths per 100 000 pregnancies, yielding relative risks (95% confidence intervals) of 0. 60 (0.37 to 0.97) and 0.51 (0.30 to 0.86). This represented reductions of 40% (P<0.04) and 49% (P<0.01) among those who received vitamin A and beta carotene. Combined, vitamin A or beta carotene lowered mortality by 44% (0.56 (0.37 to 0.84), P<0.005) and reduced the maternal mortality ratio from 645 to 385 deaths per 100 000 live births, or by 40% (P<0.02). Differences in cause of death could not be reliably distinguished between supplemented and placebo groups. CONCLUSION: Supplementation of women with either vitamin A or beta carotene at recommended dietary amounts during childbearing years can lower mortality related to pregnancy in rural, undernourished populations of south Asia.  (+info)

In the paper, the algorithm of the hierarchical cluster analysis is considered and the method is proposed to transfer this algorithm onto the parallel multiprocessor system used on modern graphics processing units (GPUs). Within the frameworks of some natural assumptions, we have estimated the run time of the algorithm in a sequential case, in a parallel case for some abstract parallel machine and for GPU. The algorithm is implemented on CUDA, allowing us to carry out the hierarchical cluster analysis much faster, than on CPU. ...
For the cluster analysis, k-means was used with an Euclidean distance as it is efficient, fast, and can handle large datasets [9]. However, k-means requires the number of clusters (k) to be determined by the user. Hierarchical methods, on the other hand, can be analyzed for the optimal cluster number but struggle with large datasets [10]. We therefore applied hierarchical cluster analysis to 10 random samples of 3000 patients to identify the optimal number of clusters. This information was then used to perform a k-means analysis of the full dataset, to create the final clusters.. The hierarchical cluster analysis was conducted using Stata 14 software [11]. Wards method was used as it aims to minimize the cluster sum of squares and can therefore be considered a hierarchical analogue for k-means [12]. For each of the 10 random samples the pseudo F statistic, as defined by Calinski and Harabasz [13], and the Duda and Hart Je(2)/Je(1) index [14], were calculated for 4- to 12-cluster solutions. The ...
Hierarchical Cluster Analysis. Dear Listers, I am familar with the SPSS routines to do cluster analysis, but Im wondering if anyone is familiar with how this method compares to geospatial...
K-means is for interval data. So, using it means that you assume Likert rating scale is interval. OK, you have your right for this, albeit puristic people will frown and mutter likerts are ordinal, likerts are ordinal..... Next, K-means is expected to be better, more discriminating, for finely grained scale (a one closer to be continuous). This is as in everywhere in analysis: thin scales are usually better than rude scales. So, generally, 5-point scale would be better than 4-point scale.. Still, you should think twice, because psychometrically 4-point and 5-point rating scales behave not identically. 4-point scale is visually opinion-disruptive, having no central point; it is perceived as forcing to take a stand. That might be bad in one contexts and good in other contexts, in the end, the decision is yours. 5-point scale suffers from having number 5 at the edge - which is culturally prominant in many societies, and it has another similarly magic number 3 (right in the middle!). Both can ...
Hierarchical cluster analysis of the 14 subgroups identified from the test dataset using the average linkage distance.The 14 subgroups consist of 5 major subgro
Hierarchical cluster analysis. Using a hierarchical method, a clustering graph was created from those miRNAs with increased (red) or decreased (blue) fold of ex
A software that creates a virtual replica of our local cluster of stars and draws lines between the stars allowing the user to appreciate the geometry created by the lines by flying through it.. It would also allow you to hold your phone up to the sky and see the stars with the lines between them exaggerated in 3D so you can get a sense of the relationships between them and visualize yourself as more inside the shapes that the stars create.. You could also print out a model of the local cluster with the lines between them on a 3D printer so blind people can get a sense of our local cluster homes structure ...
Next click on Cluster Analysis, set your threshold and click Start. You will now see a cluster analysis processing job in your work list and can monitor its progress. The time it takes to complete the analysis will depend on the number of items in your case. Typically this goes pretty quick. To give you a benchmark I have a demo case with approximately 6,000 items in it and clustering takes about 5 minutes to complete.. Analyzing Results So what does this thing do??? Cluster analysis will identify groups, or clusters, of documents with similar content. For every cluster there will be a Pivot document which is like the root of the cluster and each similar item in the cluster will be given a Percent Similarity score. This Similarity to Pivot score tells you how similar the item is in relation to the pivot document. Another key feature of cluster analysis is that it will also identify email threads or conversations. After cluster analysis is performed the results can be viewed in the ...
Are Greek High School Students Environmental Citizens?: A Cluster Analysis Approach: Despina Sdrali, Nikolaos Galanis, Maria Goussia-Rizou, Konstadinos Abeliotis: Journal Articles
A distributed system provides for separate management of dynamic cluster membership and distributed data. Nodes of the distributed system may include a state manager and a topology manager. A state manager handles data access from the cluster. A topology manager handles changes to the dynamic cluster topology. The topology manager enables operation of the state manager by handling topology changes, such as new nodes to join the cluster and node members to exit the cluster. A topology manager may follow a static topology description when handling cluster topology changes. Data replication and recovery functions may be implemented, for example to provide high availability.
Cluster analysis and dissimilarity matrices of the Caucasian and Asian models of facial expressions. In each panel, vertical color coded bars show the k means (k = 6) cluster membership of each model. Each 41-dimensional model (n = 180 per culture) corresponds to the emotion category labelled above (30 models per emotion). The underlying grayscale dissimilarity matrices represent the Euclidean distances between each pair of models, used as inputs to k-means clustering. Note that, in the Caucasian group, the lighter squares along the diagonal indicate higher model similarity within each of the six emotion categories compared with the East Asian models. Correspondingly, k-means cluster analysis shows that the Western Caucasian models form six emotionally homogenous clusters... In contrast, the Asian models show considerable model dissimilarity within each emotion category and overlap between categories. ...
Cluster Group Development: 10.4018/978-1-7998-3416-8.ch005: Cluster groups are those organizations or individuals who have similar businesses and relationships. Clusters usually form naturally and organically due to
The course covers cluster analysis concepts and methods in SPSS. It is aimed at those with an interest in developing practical skills to implement clustering techniques and those with an interest in area typologies and classifications.. Participants will develop an understanding of clusterIng methods and procedures in SPSS. By the end of the course they will be able to carry out preliminary analysis to select and transform variables for cluster analysis, choose a clustering method, evaluate and choose cluster solutions, interpret clusters and present cluster analysis results. Hierarchical and non-hierarchical cluster analysis will be applied to 2011 Census local area data to produce an area classification to group areas with similar overall population characteristics into clusters.. Participants should have familiarity with SPSS and an understanding of basic data analytical techniques including correlation and regression analysis. ...
TY - CHAP. T1 - A Brief history of cluster analysis. AU - Murtagh, Fionn. PY - 2015/1/1. Y1 - 2015/1/1. N2 - Beginning with some statistics on the remarkable growth of cluster analysis research and applications over many decades, we proceed to view cluster analysis in terms of its major methodological and algorithmic themes. We then review the early, influential domains of application. We conclude with a short list of surveys of the area, and an online resource with scanned copies of early pioneering books.. AB - Beginning with some statistics on the remarkable growth of cluster analysis research and applications over many decades, we proceed to view cluster analysis in terms of its major methodological and algorithmic themes. We then review the early, influential domains of application. We conclude with a short list of surveys of the area, and an online resource with scanned copies of early pioneering books.. UR - http://www.scopus.com/inward/record.url?scp=85054281142&partnerID=8YFLogxK. U2 - ...
Co-clustering is a class of unsupervised data analysis techniques aiming at extracting the underlying dependency structure between the rows and columns of a data table in the form of homogeneous blocks, known as co-clusters. These techniques can be distinguished into those that aim at simultaneously clustering the instances and variables, and those that aim at clustering the values of two or more variables of a data set. Most of these techniques are limited to variables of the same type, and are hardly scalable to large data sets while providing easily interpretable clusters and co-clusters. Among the existing value based co-clustering approaches, MODL is suitable for processing large data sets with several numerical or categorical variables. In this thesis, we propose a value based approach, inspired by MODL, to perform a simultaneous clustering of the instances and variables of a data set with potentially mixed-type variables. The proposed co-clustering model provides a Maximum A Posteriori based
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Additional free disk space is required to run the program (for temporary files). The amount of space needed for temporary files depends on the number of users, the expected size of the .sav file, and the procedure. You can use the following formula to estimate the space needed: ,number of users, * ,.sav file size, * ,factor for procedures,, where ,factor for procedures, can range from 1 to 2.5. For example, for procedures like K-Means Cluster Analysis (QUICK CLUSTER), Classification Tree (TREE), and Two-Step Cluster Analysis (TWOSTEP CLUSTER), the ,factor for procedures, is closer to 1 than 2.5. If sorting is involved, it is 2.5. So, if you have four users, the expected .sav file size is 100 MB, and sorting is involved, you should allow 1 GB (4 Ã- 100 MB Ã- 2.5) of storage for temporary files ...
Cluster analysis is a research tool suitable to determine natural groupings within a large group of observation. Cluster analysis segments the survey sample, for example users, customers or companies as survey respondents, on a smaller number of groups.. Respondents whose answers are very similar should be in the same cluster while respondents with significantly different answers should be in different clusters. Ideally, in each group should exist a very similar profile towards certain characteristics (for example, opinions and behaviour), while the profile of the respondents from different clusters should be different.. The main advantage of this analysis is that it may propose a grouping which couldnt be easily visible, for example needs of specific groups or segments of the market.. Cluster analysis s often used in market research to describe and quantify consumer segments. This allows client to adapt their strategic approach to the specific needs of consumers rather than applying a general ...
TY - JOUR. T1 - Cluster validity and uncertainty assessment for self-organizing map pest profile analysis. AU - Roigé, Mariona. AU - McGeoch, Melodie A.. AU - Hui, Cang. AU - Worner, Susan P.. PY - 2017/3/1. Y1 - 2017/3/1. N2 - Pest risk assessment (PRA) comprises a set of quantitative and qualitative tools to protect productive ecosystems from the impacts of unwanted biological invasions. Self-organizing maps for pest profile analysis (SOM PPA) is a methodological approach aimed to support PRA. It is based on cluster analysis and extracts information out of current distributions of insect crop pests world-wide, allowing the analyst to generate a list of potential risk species for a target region. Self-organizing maps for pest profile analysis currently lacks of a measure of performance able to provide a level of confidence for its outputs. In this study, we investigate ζ diversity as an ecologically meaningful and generalizable metric of similarity. The application of ζ allowed us to ...
Downloadable! Relevance of forming clusters development management contours used as their available potential development level management levers has been proved. The approach to representation of cluster structures as a system of atomic elements has been offered. The theoretical and methodological grounds of approach to the multiagent modeling of business entities interactions. These entities are involved in several chains of value creation. Cluster structure is represented as logistic chains aggregate. Balanced scorecard system and viable systems model have been chosen as tools of management organization.
Objective: To investigate if patterns of CSF biomarkers (T-tau, P-tau, and Aβ42) can predict cognitive progression, outcome of cholinesterase inhibitor (ChEI) treatment, and mortality in Alzheimer disease (AD).. Methods: We included outpatients with AD (n = 151) from a prospective treatment study with ChEI. At baseline, patients underwent cognitive assessments and lumbar puncture. The patients were assessed longitudinally. The 5-year survival rate was evaluated. CSF-Aβ42, T-tau, and P-tau were analyzed at baseline. K-means cluster analysis including the 3 CSF biomarkers was carried out.. Results: Cluster 1 contained 87 patients with low levels of Aβ42 and relatively low levels of T-tau and P-tau. Cluster 2 contained 52 patients with low levels of Aβ42 and intermediate levels of T-tau and P-tau. Cluster 3 contained 12 patients with low levels of Aβ42 and very high levels of CSF T-tau and P-tau. There were no differences between the clusters regarding age, gender, years of education, baseline ...
Under what conditions is ethical consumption a high-status practice? Using unique food consumption survey data on aesthetic and ethical preferences, we investigate how these orientations to food are related. Existing research on high-status food consumption points to the foodie, who defines good taste through aesthetic standards. And emergent evidence suggests the ethical consumer, whose consumption is driven by moral principles, may also be a high-status food identity. However, ethical consumption can be practiced in inexpensive and subcultural ways that do not conform to dominant status hierarchies (e.g., freeganism). In order to understand the complex cultural terrain of high-status consumption, we investigate how socioeconomic status (SES) is related to foodie and ethical consumer preferences and practices. Using a k-means cluster analysis of intercept survey data from food shoppers in Toronto, we identify four distinct clusters representing foodies, ethical consumers, ethical foodies, ...
We sought to investigate (1) the characteristics of epileptiform discharge (ED) duration and inter-discharge interval (IDI) and (2) the influence of vigilance state on the ED duration and IDI in genetic generalized epilepsy (GGE). In a cohort of patients diagnosed with GGE, 24-hour ambulatory EEG recordings were performed prospectively. We then tabulated durations, IDI, and vigilance state in relation to all EDs captured on EEGs. We used K-means cluster analysis and finite mixture modeling to quantify and characterize the groups of ED duration and IDI. To investigate the influence of sleep, we calculated the mean, median, and standard error of the mean in each population from all subjects for sleep state and wakefulness separately, followed by the Kruskal-Wallis test to compare the groups. We analyzed 4679 epileptiform discharges and corresponding IDI from 23 abnormal 24-hour ambulatory EEGs. Our analysis defined two populations of ED durations and IDI; short and long. In all populations, both ED
Objectives. The primary aim of this study was to describe the geography of serious mental illness (SMI) - type 2 diabetes comorbidity(T2D) in the Illawarra-Shoalhaven region of NSW, Australia and to identify the significant clusters and their locations. The secondary objective was to determine the geographic concordance if any, between the comorbidity and the single diagnosis of SMI and T2D. Methods. Spatial analytical techniques were applied to clinical data to explore the above aims. The geographic variation in comorbidity was determined by Morans I at the global level and the local clusters of significance were determined by Local Indicators of Spatial Association (LISA) and Spatial scan statistic. Choropleth hotspot maps were created to visually assess the geographic convergence of SMI, diabetes and their comorbidity. Additionally, we used bivariate LISA to identify coincident areas with higher rates of both SMI and T2D. Results. The study identified significant geographic variations in the ...
Cluster Profiles identifies significant cluster means in all the variables simultaneously. In the example, the Response Rate variable is highlighted in red. It shows at a glance how the cluster means for all the variables compare at each level from 1 to 6 clusters.. Its easy to see that the 2 cluster level is differentiated on the Response Rate, with means of 2.02 in cluster -2 and 6.89 in cluster +2. The equivalent decision tree rule for the first split, or final fusion, would be: Response Rate , 4.5.. At the next level the first variable differentiates clusters -3 and +3. At the following cluster level, the first 3 variables are correlated in differentiating clusters -4 (high) and +4 (low), with variable 2 dominating.. Bear in mind that this is not a decision tree. Clusters are formed on all variables simultaneously, so the analysis is multivariate at each clustering level.. This example illustrated the following ClustanGraphics features: k-means analysis with outlier deletion on a large ...
Encephalitis is an acute clinical syndrome of the central nervous system (CNS), often associated with fatal outcome or permanent damage, including cognitive and behavioural impairment, affective disorders and epileptic seizures. Infection of the central nervous system is considered to be a major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. However, a large proportion of cases have unknown disease etiology. We perform hierarchical cluster analysis on a multicenter England encephalitis data set with the aim of identifying sub-groups in human encephalitis. We use the simple matching similarity measure which is appropriate for binary data sets and performed variable selection using cluster heatmaps. We also use heatmaps to visually assess underlying patterns in the data, identify the main clinical and laboratory features and identify potential risk factors associated with encephalitis. Our results identified fever, personality and behavioural change,
Yeah, I started working on a k-means cluster module recently. However, at the moment theres no way to add the cluster groups to the spreadsheet making the analysis not very useful. Once weve implemented adding analyses data to the spreadsheet, Ill start working on it again ...
The aim of the study is to compare TIMSS 2011 proficiency levels with the proficiency levels defined by the researchers using cluster analysis for Turkey, Korean, Norway, and Morocco in 4th and 8 th grades in the fields of science and mathematics. Therefore, it is tried to be reached that these cut-off scores for each country can serve the evaluation of each country itself. For this research, the data gathered from related countries students was taken from TIMSS 2011 database. Statistical analysis was performed with SPSS Version 21.0 statistic software package. The cut-off scores for these four countries selected in this study for each grade level and course type were defined using cluster analysis. Then, proficiency levels according to these cut-off scores were compared to the general TIMSS 2011 proficiency levels, and so the difference between these levels and percentage of agreement have been examined. According to the results, cut-off scores set by using cluster analysis for
Please note that the information and the Excel template for running cluster analysis on this website have been provided free and in good faith. Testing has indicated that the template appears to work as required. Of course, as highlighted in the discussion of cluster analysis and varying results, this statistical technique will vary somewhat according to start (seed) points.. This website is primarily designed for use by university students in their studies. It only allows for up to 100 respondents and is, therefore, not capable of analyzing a large customer database - it is primarily a learning tool.. If you intend to use the free Excel template and/or the information on this website for business purposes, it is strongly recommended that you also seek the advice of a qualified marketing research consultant or data analyst with expertise in cluster analysis and developing market segments to help guide you.. Please contact me if you have any questions regarding this disclaimer, or if you require ...
Abstract:. During the last decades it has been established that breast cancer arises through the accumulation of genetic and epigenetic alterations in different cancer related genes. These alterations confer the tumor oncogenic abilities, which can be resumed as cancer hallmarks (CH). The purpose of this study was to establish the methylation profile of CpG sites located in cancer genes in breast tumors so as to infer their potential impact on 6 CH: i.e. sustained proliferative signaling, evasion of growth suppressors, resistance to cell death, induction of angiogenesis, genome instability and invasion and metastasis. For 51 breast carcinomas, MS-MLPA derived-methylation profiles of 81 CpG sites were converted into 6 CH profiles. CH profiles distribution was tested by different statistical methods and correlated with clinical-pathological data. Unsupervised Hierarchical Cluster Analysis revealed that CH profiles segregate in two main groups (bootstrapping 90-100%), which correlate with breast ...
In many applications, it is of interest to uncover patterns from a high-dimensional data set in which the number of features, p, is larger than the number of observations, n. We consider the areas of graph estimation and cluster analysis, which are often used to construct gene expression network and to partition the observations or features into subgroups, respectively. For graph estimation, we propose a framework to estimate graphical models with a few hub nodes that are densely-connected to many other nodes. We apply our framework to three widely used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. For cluster analysis, we propose a novel methodology for partitioning both observations and features into groups simultaneously, which we refer to as sparse biclustering. We also propose a framework to account for the correlation among the observations and features when we perform sparse biclustering. In addition, we study the ...
Although the vast majority of patients with a myelodysplastic syndrome (MDS) suffer from cytopenias, the bone marrow is usually normocellular or hypercellular. Apoptosis of hematopoietic cells in the bone marrow has been implicated in this phenomenon. However, in MDS it remains only partially elucidated which genes are involved in this process and which hematopoietic cells are mainly affected. We employed sensitive real-time PCR technology to study 93 apoptosis-related genes and gene families in sorted immature CD34+ and the differentiating erythroid (CD71+) and monomyeloid (CD13/33+) bone marrow cells. Unsupervised cluster analysis of the expression signature readily distinguished the different cellular bone marrow fractions (CD34+, CD71+ and CD13/33+) from each other, but did not discriminate patients from healthy controls. When individual genes were regarded, several were found to be differentially expressed between patients and controls. Particularly, strong over-expression of BIK (BCL2-interacting
How would you identify a small number of face images that together accurately represent a data set of face images? How would you identify a small number of sentences that accurately reflect the content of a document? How would you identify a small number of cities that are most easily accessible from all other cities by commercial airline? How would you identify segments of DNA that reflect the expression properties of genes? Data centers, or exemplars, are traditionally found by randomly choosing an initial subset of data points and then iteratively refining it, but this only works well if that initial choice is close to a good solution. Affinity propagation is a new algorithm that takes as input measures of similarity between pairs of data points and simultaneously considers all data points as potential exemplars. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We have used affinity propagation to solve ...
Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy |i |c|/i|-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number |svg style=vertical-align:-0.1638pt;width:7.0250001px; id=M1 height=7.9499998 version=1.1 viewBox=0 0 7.0250001 7.9499998 width=7.0250001 xmlns:xlink=http://www.w3.org/1999/xlink xmlns=http://www.w3.org/2000/svg| |g transform=matrix(.017,-0,0,-.017,.062,7.675)||path id=x1D450 d=M383 397q0 -32 -35 -49q-12 -6 -23 8q-37 45 -84 45t-90 -71q-40 -65 -40 -167q0 -57 22 -86t59 -29q38 0 81.5 24.5t69.5 51.5l16 -21q-44 -53 -104 -84t-109
Low-rate denial of service (LDoS) attacks send attacking bursts intermittently to the network which can severely degrade the victim systems Quality of Service (QoS). The low-rate nature of such attacks complicates attack detection. LDoS attacks repeatedly trigger the congestion control mechanism, which can make TCP traffic extremely unstable. This paper investigates the network traffic characteristics, in which variance and entropy are used to evaluate the TCP traffics characteristics, and the ratio of UDP traffic to TCP traffic (UTR) is also analyzed. Thus, a detection method combining two-step cluster analysis and UTR analysis is proposed. Through two-step cluster analysis which is one of the machine learning algorithms, network traffic is divided into multiple clusters and then clusters subjected to LDoS attacks are determined using UTR analysis. NS2 simulation platform and test-bed network environment aim to evaluate the detection approachs performance. To better assess the effectiveness of the
In cluster analysis, one does not start with any apriori notion of group characteristics. The definition of clusters emerges entirely from the cluster analysis - i.e. from the process of identifying clumps of objects. Clustering is used in many fields, including customer segmentation. An airline analyzing its customer data, for example, might find that there is a distinct cluster of passengers with the following characteristics: travel weekly, travel mainly one or two short-haul routes, book at the last minute, dont check bags.. ...
Cluster analysis is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest. Each group contains observations with similar profile according to a specific criteria. Similarity between observations is defined using some inter-observation distance measures including Euclidean and correlation-based distance measures. In the literature, cluster analysis is referred as
I have been in previous post using the ChemoSpec package for some oil data (olive and sunflower). My spectra has now a range from 1100nm to 2200nm and is raw (not treated mathematically) . I want to start using the ChemoSpec package to start using the Hierarchical Cluster Analysis in order to see some cluster in my data. Of course I hope to see the olive oil in one cluster and the sunflower in the other. But probably other clusters can appear. ...
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Aims and Objectives Have a working knowledge of the ways in which similarity between cases can be quantified (e.g. single linkage, complete linkage and average linkage). Be able to produce and interpret dendrograms produced by SPSS. Know that different methods of clustering will produce different cluster structures. What is Cluster Analysis? We have already seen…
This part presents advanced clustering techniques, including: hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and density-based clustering. Hierarchical k-means clustering. The hierarchical k-means clustering is an hybrid approach for improving k-means results. Fuzzy clustering Fuzzy clustering is also known as soft method. Standard clustering (K-means, PAM) approaches produce partitions, in which each observation belongs to only one cluster. This is known as hard clustering. In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster. Model-based clustering In model-based clustering, the data are viewed as coming from a distribution that is mixture of two ore more clusters. It finds best fit of models to data and estimates the number of clusters. DBSCAN: Density-Based Clustering The density-based clustering (DBSCAN is a partitioning method that has been
This paper investigates the trade competitiveness of the new emerging Southern economies - China, India, Brazil and South Africa (CIBS) - with respect to their main global partners. Starting from the commonly held view that countries with trade patterns similar to those of emerging countries are likely to suffer losses, we propose a multidimensional approach based on cluster analysis, both crisp and fuzzy, as an alternative strategy for assessing similarity in global trade patterns. On the basis of key trade characteristics drawn from the diverse strands of trade theory, we assess the relative position of CIBS within global trade patterns and their evolution over time. Unlike previous studies, our results do not support the hypothesis of the presence of a competitiveness threat from Southern emerging countries towards the main industrialised economies.. ...
Download full project about Identifying Hidden Patterns in Students‟ Feedback through Cluster Analysis . Your business software is ready for download . You can use it for your own company / Office / home without any cost. We provide free business software for our visitor. The software is develop by using different model such as waterfall life-cycle ,traditional ,classic etc Identifying Hidden Patterns in Students‟ Feedback through Cluster Analysis is a large and time consuming project. So, Our aim is to help all business vendors by sharing our best. We want your help by joining our community. You will get your project as you desire ...
I am trying to run cluster analysis on a long stream of back trajectories. I have 5, 7, and 10 day lengths and at multiple heights. I have tried running the cluster analysis several times with my files and cant seem to get them to read. I have tried using a few different sets of trajectory files. The groups all start at the same height and within the same year. No matter which ones I use, I receive one of two error messages ...
TY - JOUR. T1 - A quantitative analysis of educational data through the comparison between hierarchical and not-hierarchical clustering. AU - Fazio, Claudio. AU - Battaglia, Onofrio Rosario. AU - Di Paola, Benedetto. PY - 2017. Y1 - 2017. N2 - Many research papers have studied the problem of taking a set of data and separating it into subgroups through the methods of Cluster Analysis. However, the variables and parameters involved in Cluster Analysis have not always been outlined and criticized, especially in the field of Science Education. Moreover, in the field of Science Education, a comparison between two different Clustering methods is not discussed in the literature. In this paper two different Cluster Analysis methods are described and the variables and parameters involved are discussed in order to clarify the information that they can supply. The clustering results obtained by using the two methods are compared and showed a good coherence between them. The results are interpreted and ...
The Cluster Analysis is an explorative analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis.
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According to modern historiography, around the year 1000, the northern Hungarian duchy with its capital in Nitra was divided into four parts which can be predicted with more or less certainty as direct descendants of tribes both within and beyond the Great Moravian Empire: Nitra, Hont, Váh, Borsod, and, in addition, in the very eastern part of today's Slovak Republic, there was a tribe which was not the part of Nitra Duchy. The consideration of tribes is important. A tribe speaks a dialect. However, the division of Slovakia was probably more complicated and on the basis of both genealogical and linguistic research we have to accept at least seven tribes and dialects in Slovakia around the year 1000. This division is supported also by the research of Proto-Slavic lexis in Old Slovak. The paper deals with hierarchical cluster analysis of this lexis and offers a solution to the genealogical problem of Slovak language. According to it, Old Slovak can be divided into eastern and west-central ...
Objective of any biclustering algorithm in microarray data is to discover a subset of genes that are expressed similarly in a subset of conditions. The boundaries of biclusters usually overlap as genes and conditions may belong to different biclusters with different membership degrees. Hence the notion of fuzzy sets is useful for discovering such overlapping biclusters. In this article an attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance. A novel variable string length encoding has been proposed in this regard that encodes multiple biclusters in a single string. Also a new performance measure that reflects how a bicluster is statistically distinguished from the background is proposed. Performance of the proposed algorithm has been compared with some well known biclustering algorithms. © 2008 IEEE.. ...
Downloadable (with restrictions)! Supporting services augment the value of a businesss core service, provide points of differentiation, and create a competitive advantage over competitors. Fitness clubs offer a number of supporting services, including sport participation opportunities. Fitness tests are a common supporting service. This study examined interest in fitness tests and related supporting services. Moreover, because customised programs are harder to imitate, optimal combinations of desired services were investigated. Further, K-means cluster analysis identified seven meaningfully differentiated customer groups. MANOVA and chi-square analyses indicated that clustered groups differed based on demographic and psychographic variables. The study demonstrates that (1) consumers desire supporting services, (2) distinct bundles of supporting services can be identified, and (3) consumers desiring distinct bundles of services are have distinct demographic and psychographic profiles. Fitness providers
Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene ...
Analysis of Chlorinated Hydrocarbon Concentration Data from Thousands of Groundwater Wells Using a Density-Based Cluster Analysis Approach
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering
article{c7645192-ef05-4b8e-bbfb-db2b0fc93f4e, abstract = {ObjectiveHematopoietic stem cell transplantation (HSCT) is curative in several life-threatening pediatric diseases but may affect children and their families inducing depression, anxiety, burnout symptoms, and post-traumatic stress symptoms, as well as post-traumatic growth (PTG). The aim of this study was to investigate the co-occurrence of different aspects of such responses in parents of children that had undergone HSCT. MethodsQuestionnaires were completed by 260 parents (146 mothers and 114 fathers) 11-198 months after HSCT: the Hospital Anxiety and Depression Scale, the Shirom-Melamed Burnout Questionnaire, the post-traumatic stress disorders checklist, civilian version, and the PTG inventory. Additional variables were also investigated: perceived support, time elapsed since HSCT, job stress, partner-relationship satisfaction, trauma appraisal, and the childs health problems. A hierarchical cluster analysis and a k-means cluster ...
Cluster Analysis Menggunakan Algoritma Fuzzy C-means dan K-means Untuk Klasterisasi dan Pemetaan Lahan Pertanian di Minahasa Tenggara
Background: The timely and accurate identification of symptoms of acute coronary syndrome (ACS) is a challenge forpatients and clinicians. It is unknown whether response times and clinical outcomes differ with specific symptoms. We sought toidentify which ACS symptoms are related symptom clusters and to determine if sample characteristics, response times, and outcomes differ among symptom cluster groups. Methods: In a multisite randomized clinical trial, 3522 patients with known cardiovascular disease were followed up for 2 years. During follow-up, 331 (11%) had a confirmed ACS event. In this group, 8 presenting symptoms were analyzed using cluster analysis. Differences in symptom cluster group characteristics, delay times, and outcomes were examined. Results: The sample was predominately male (67%), older (mean 67.8, S.D. 11.6 years), and white (90%). Four symptom clusters were identified: Classic ACS characterized by chest pain; Pain Symptoms (neck, throat, jaw, back, shoulder, arm pain); ...
  This paper aims to test the hypothesis of consumer price index convergence among Iran provinces over the period from 2003 to 2016 by implementing cluster analysis and panel unit root test. Studying the price index convergence is important in several ways. First, CPI convergence is equivalent in some ways ...
With limited resources available, injury prevention efforts need to be targeted both geographically and to specific populations. As part of a pediatric injury prevention project, data was obtained on all pediatric medical and injury incidents in a fire district to evaluate geographical clustering of pediatric injuries. This will be the first step in attempting to prevent these injuries with specific interventions depending on locations and mechanisms. There were a total of 4803 incidents involving patients less than 15 years of age that the fire district responded to during 2001-2005 of which 1997 were categorized as injuries and 2806 as medical calls. The two cohorts (injured versus medical) differed in age distribution (7.7 ± 4.4 years versus 5.4 ± 4.8 years, p | 0.001) and location type of incident (school or church 12% versus 15%, multifamily residence 22% versus 13%, single family residence 51% versus 28%, sport, park or recreational facility 3% versus 8%, public building 8% versus 7%, and street
With limited resources available, injury prevention efforts need to be targeted both geographically and to specific populations. As part of a pediatric injury prevention project, data was obtained on all pediatric medical and injury incidents in a fire district to evaluate geographical clustering of pediatric injuries. This will be the first step in attempting to prevent these injuries with specific interventions depending on locations and mechanisms. There were a total of 4803 incidents involving patients less than 15 years of age that the fire district responded to during 2001-2005 of which 1997 were categorized as injuries and 2806 as medical calls. The two cohorts (injured versus medical) differed in age distribution (7.7 ± 4.4 years versus 5.4 ± 4.8 years, p | 0.001) and location type of incident (school or church 12% versus 15%, multifamily residence 22% versus 13%, single family residence 51% versus 28%, sport, park or recreational facility 3% versus 8%, public building 8% versus 7%, and street
A feature selection method in microarray gene expression data should be independent of platform, disease and dataset size. Our hypothesis is that among the statistically significant ranked genes in a gene list, there should be clusters of genes that share similar biological functions related to the investigated disease. Thus, instead of keeping N top ranked genes, it would be more appropriate to define and keep a number of gene cluster exemplars. We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. We applied mAP-KL on real microarray data, as well as on simulated data, and compared its performance against 13 other feature selection approaches. Across a variety of diseases and number of samples, mAP-KL presents competitive classification results, particularly in neuromuscular diseases, where its overall AUC score was 0
In this thesis, a mixture-model cluster analysis technique under different covariance structures of the component densities is developed and presented, to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data sets to achieve flexibility in currently practiced cluster analysis techniques. Two approaches to parameter estimation are considered and compared; one using the Expectation-Maximization (EM) algorithm and another following a Bayesian framework using the Gibbs sampler. We develop and score several forms of the ICOMP criterion of Bozdogan (1994, 2004) as our fitness function; to choose the number of component clusters, to choose the correct component covariance matrix structure among nine candidate covariance structures, and to select the optimal parameters and the best fitting mixture-model. We demonstrate our approach on simulated datasets and a real large data set, focusing on early detection
This study links empirical analysis of geographical variations in fertility to ideas of contextualising demography. We examine whether there are statistically significant clusters of fertility in Scotland between 1981 and 2001, controlling for more general factors expected to influence fertility. Our hypothesis, that fertility patterns at a local scale cannot be explained entirely by ecological socio-economic variables, is supported. In fact, there are unexplained local clusters of high and low fertility, which would be masked in analyses at a different scale. We discuss the demographic significance of local fertility clusters as contexts for fertility behaviour, including the role of the housing market and social interaction processes, and the residential sorting of those displaying or anticipating different fertility behaviour. We conclude that greater understanding of local geographical contexts is needed if we are to develop mid-level demographic theories and shift the focus of fertility ...
K-means algorithm is explained and an implementation is provided in C# and Silverlight. It includes a live demo in Silverlight so that the users can understand the working of k-means algorithm by specifying custom data points.
TY - JOUR. T1 - Dietary patterns by cluster analysis in pregnant women. T2 - relationship with nutrient intakes and dietary patterns in 7-year-old offspring. AU - Freitas-Vilela, Ana Amélia. AU - Smith, Andrew D A C. AU - Kac, Gilberto. AU - Pearson, Rebecca M. AU - Heron, Jon. AU - Emond, Alan. AU - Hibbeln, Joseph R. AU - Castro, Maria Beatriz Trindade. AU - Emmett, Pauline M. N1 - © 2016 The Authors. Maternal & Child Nutrition published by John Wiley & Sons Ltd.. PY - 2017/4. Y1 - 2017/4. N2 - Little is known about how dietary patterns of mothers and their children track over time. The objectives of this study are to obtain dietary patterns in pregnancy using cluster analysis, to examine womens mean nutrient intakes in each cluster and to compare the dietary patterns of mothers to those of their children. Pregnant women (n = 12 195) from the Avon Longitudinal Study of Parents and Children reported their frequency of consumption of 47 foods and food groups. These data were used to obtain ...
Read Constrained clustering with a complex cluster structure, Advances in Data Analysis and Classification on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Abstract: Color fusion MRI is being investigated for its value in automatic segmentation of tissues. An existing color fusion MRI data set of the liver, pancreas, and kidney of a normal male volunteer was analyzed both visually and statistically. Automatic tissue segmentation can allow better differentiation of abdominal pathologies, as well as pathologies associated with other organs. My research hypothesis is that fuzzy c-means clustering can be used to quantify the confidence levels of correct classification of renal, pancreatic, and hepatic tissues visualized by the color fusion MRI method. Results from data show that fuzzy c-means clustering can be used to validate the correctness of classification of abdominal tissues that are visualized by color fusion MRI.
Longitudinal data refer to the situation where repeated observations are available for each sampled object. Clustered data, where observations are nested in a hierarchical structure within objects (wi
Forty-two native, new and foreign breeds were analyzed for 18 traits. Principal component (PC) analysis showed that the first three PCs accounted for 82.6% of the total variation. The first PC is a Size and Weight Factor (SWF) and accounts for 50.5% of the total variation. The second PC is a Skin and Bone Factor (SBF) and accounts for 20.8% of the variation. The third PC is a Reproduction and Fat Factor (RFF) and accounts for 11.3% of the total variation. Non-lean meat carcass traits (skin, bone and fat) are associated with reproductive performance. Plotting SBF against SWF is useful in grouping of breed groups. This grouping is in agreement with that obtained by cluster analysis. Breeds from the same geographical area tend to be in the same performance group, suggesting genetic connections in the past. Cluster analysis indicated six genetic types. New breeds showed the shortest genetic distance to the foreign contributor breeds ...
In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we --a team of visualization scientists and meteorologists-- deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows ...
In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T(a) and T(1) tumors, ...
The clustering methods have to assume some cluster relationship among the data objects that they are applies on. Similarity between a pai...
COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters
We have analyzed genetic data for 326 microsatellite markers that were typed uniformly in a large multiethnic population-based sample of individuals as part of a study of the genetics of hypertension (Family Blood Pressure Program). Subjects identified themselves as belonging to one of four major racial/ethnic groups (white, African American, East Asian, and Hispanic) and were recruited from 15 different geographic locales within the United States and Taiwan. Genetic cluster analysis of the microsatellite markers produced four major clusters, which showed near-perfect correspondence with the four self-reported race/ethnicity categories. Of 3,636 subjects of varying race/ethnicity, only 5 (0.14%) showed genetic cluster membership different from their self-identified race/ethnicity. On the other hand, we detected only modest genetic differentiation between different current geographic locales within each race/ethnicity group. Thus, ancient geographic ancestry, which is highly correlated with ...
Compared with individually randomised trials, cluster randomised trials are more complex to design, require more participants to obtain equivalent statistical power, and require more complex analysis. The methodological issues in cluster randomised trials have been widely discussed.7 9 In brief, observations on individuals in the same cluster tend to be correlated (non-independent), and so the effective sample size is less than the total number of individual participants.. The reduction in effective sample size depends on average cluster size and the degree of correlation within clusters, known as the intracluster (or intraclass) correlation coefficient (ρ). The intracluster correlation coefficient is the proportion of the total variance of the outcome that can be explained by the variation between clusters. To retain power, the sample size should be multiplied by 1+(m - 1)ρ, called the design effect, where m is the average cluster size. Hayes and Bennett describe a related coefficient of ...
The distributed K-means cluster algorithm which focused on multidimensional data has been widely used. However, the current distributed K-means clustering algor
In this study we have shown biclustering to be a useful approach to identifying subgroups of tumours, based on the use of stratified biomarkers that are personalised to specific subsets of patients. Biclustering determines gene modules and related clinical features which are important in determining phenotypic and clinical outcomes in those patients, but not in others.. In particular, we have applied biclustering to a large breast cancer expression data set that includes careful clinical annotations, and have used this method to identify clusters of breast tumours conditional on common expression profiles across a set of genes. We also demonstrated that biclusters do not simply recapitulate any obvious single, known clinical covariate (Figure 3 and Additional file 1: Figure S1), but instead represent a group of tumours co-expressing a set of genes that are associated with similar clinical presentation and give rise to recurrence risk. We found that biclusters have strong prognostic association ...
Let us now take a closer look at the results. Clik on the picture on the left to get to an interactive 3d-graph of the 4-cluster solution for which the R-code can be found below. The 4-cluster solution yields 4 ellipsoids aiming to reflect the areas with high observation densities for the clusters. These ellispoids should contribute to the ease of reading the graph, the actual observations are still represented by differently coloured dots just like in the 2-dimensional plot we used for exploration. The three upper clusters in the picture share a comparable level of Monetary Value and Recency. The dark blue ellispoid stand out of the three as it reflects higher Frequeny. The lower ellipsoid reflects observations that rank relatively low on all of the three RFM variables (remember, the higher the recency, the worse - knowing that we are working with a dataset of good donors). The video below contains a fixed-axis rotation. ...
A comprehensive study of the lattice dynamics, elastic moduli, and liquid metal resistivities for 16 simple metals in the bcc and fcc crystal structures is made using a density-based local pseudopotential. The phonon frequencies exhibit excellent agreement with both experiment and nonlocal pseudopotential theory. The bulk modulus is evaluated by the long wave and homogeneous deformation methods, which agree after a correction is applied to the former. Calculated bulk and Voigt shear moduli are insensitive to crystal structure, and long-wavelength soft modes are found in certain cases. Resistivity calculations confirm that electrons scatter off the whole Kohn-Sham potential, including its exchange-correlation part as well as its Hartree part. All of these results are found in second-order pseudopotential perturbation theory. However, the effect of a nonperturbative treatment on the calculated lattice constant is not negligible, showing that higher-order contributions have been subsumed into the ...
An urban land-cover classification of the 900 km(2) comprising the UK West Midland metropolitan area was generated for the purpose of facilitating stratified environmental survey and sampling. The classification grouped the 900 km(2) into eight urban land-cover classes. Input data to the classification algorithms were derived from spatial land-cover data obtained from the UK Centre for Ecology and Hydrology, and from the UK Ordnance Survey. These data provided a description of each km(2) in terms of the contributions to the land cover of 25 attributes (e.g. open land, urban, villages, motorway, etc.). The dimensionality of the land-cover dataset was reduced using principal component analysis, and eight urban classes were derived by cluster analysis using an agglomeration technique on the extracted components. The resulting urban land-cover classes reflected groupings of 1 km(2) pixels with similar urban land morphology. Uncertainties associated with this agglomerative classification were ...
This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem.. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating ...
Fixes a problem where a clustering model that uses the K-means algorithm generates different results that are affected by PredictOnly columns in SQL Server 2008 R2 Analysis Services.
TEDDER, Michelle J. et al. Classification and mapping of the composition and structure of dry woodland and savanna in the eastern Okavango Delta. Koedoe [online]. 2013, vol.55, n.1, pp.00-00. ISSN 2071-0771.. The dry woodland and savanna regions of the Okavango Delta form a transition zone between the Okavango Swamps and the Kalahari Desert and have been largely overlooked in terms of vegetation classification and mapping. This study focused on the species composition and height structure of this vegetation, with the aim of identifying vegetation classes and providing a vegetation map accompanied by quantitative data. Two hundred and fifty-six plots (50 m χ 50 m) were sampled and species cover abundance, total cover and structural composition were recorded. The plots were classified using agglomerative, hierarchical cluster analysis using group means and Bray-Curtis similarity and groups described using indicator species analysis. In total, 23 woody species and 28 grass species were recorded. ...
Chronic pain represents a major health problem among older people. The aims of the present study were to: (i) identify various profiles of pain and distress experiences among older patients; and (ii) compare whether background variables, sense of coherence, functional ability and experiences of interventions aimed at reducing pain and distress varied among the patient profiles. Interviews were carried out with 42 older patients. A cluster analysis yielded three clusters, each representing a different profile of patients. Case illustrations are provided for each profile. There were no differences between the clusters, regarding intensity and duration of pain. One profile, with subjects of advanced age, showed a decreased functional ability and favourable scores in most of the categories of pain and distress. Another profile of patients showed favourable mean scores in all categories. The third cluster of patients showed unfavourable scores in most categories of pain and distress. There appears to ...
This paper deals with several problems in cluster analysis. It appears that the suggested solutions have not been considered in current literature. First, the author proposes the use of a permuted matrix as a tool for interpretation of clusters generated by hierarchical agglomerative clustering algorithms. Second, a new method of defining similarity between a pair of clusters is shown. This method leads to a new class of hierarchical agglomerative clustering. Third, two criteria are defined to optimize dendrograms that are outputs of hierarchical clustering.. This paper has been presented at the Task Force Seminar Session on New Advances in Decision Support Systems, Laxenburg, Austria, November 3-5, 1986.. ...
In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors. ...
Clustering or cluster analysis is a type of data analysis. The analyst groups objects so that objects in the same group (called a cluster) are more similar to each other than to objects in other groups (clusters) in some way. This is a common task in data mining. ...
TY - JOUR. T1 - The XMM Cluster Survey. T2 - X-ray analysis methodology. AU - Lloyd-Davies, E. J.. AU - Romer, A. Kathy. AU - Mehrtens, Nicola. AU - Hosmer, Mark. AU - Davidson, Michael. AU - Sabirli, Kivanc. AU - Mann, Robert G.. AU - Hilton, Matt. AU - Liddle, Andrew R.. AU - Viana, Pedro T. P.. AU - Campbell, Heather C.. AU - Collins, Chris A.. AU - Dubois, E. Naomi. AU - Freeman, Peter. AU - Harrison, Craig D.. AU - Hoyle, Ben. AU - Kay, Scott T.. AU - Kuwertz, Emma. AU - Miller, Christopher J.. AU - Nichol, Robert C.. AU - Sahlén, Martin. AU - Stanford, S. A.. AU - Stott, John P.. PY - 2011/11/21. Y1 - 2011/11/21. N2 - The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5776 XMM observations used to construct the current XCS ...
My presentation aims at showing how these limitations can be solved by means of affinity propagation clustering. This is a mathematical method that is able to uses the phylogenetic distance matrix to allocate sequences to generic clusters. I will present you how affinity propagation clustering was applied to the distance matrices derived from the RABV full genome sample sets, resulting in a cluster structure which strongly corresponds to the structure of the Maximum Likelihood-based phylogenetic tree. At the end of my presentation I would like to discuss on strategies to implement a workflow based on this method to validate evidence for space-dependent clustering of rabies virus sequences ...
My presentation aims at showing how these limitations can be solved by means of affinity propagation clustering. This is a mathematical method that is able to uses the phylogenetic distance matrix to allocate sequences to generic clusters. I will present you how affinity propagation clustering was applied to the distance matrices derived from the RABV full genome sample sets, resulting in a cluster structure which strongly corresponds to the structure of the Maximum Likelihood-based phylogenetic tree. At the end of my presentation I would like to discuss on strategies to implement a workflow based on this method to validate evidence for space-dependent clustering of rabies virus sequences ...
Learn Disease Clusters from Johns Hopkins University. Do a lot of people in your neighborhood all seem to have the same sickness? Are people concerned about high rates of cancer? Your community may want to explore the possibility of a disease ...
The aim of this study is to investigate the profiles of students in MIS department by performing cluster analysis on various dimensions of academic abilities
To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the
Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life science
Multimorbidity is highly prevalent in the elderly and relates to many adverse outcomes, such as higher mortality, increased disability and functional decline. Many studies tried to reduce the heterogeneity of multimorbidity by identifying multimorbidity clusters or disease combinations, however, the internal structure of multimorbidity clusters and the linking between disease combinations and clusters are still unknown. The aim of this study was to depict which diseases were associated with each other on person-level within the clusters and which ones were responsible for overlapping multimorbidity clusters. The study analyses insurance claims data of the Gmünder ErsatzKasse from 2006 with 43,632 female and 54,987 male patients who were 65 years and older. The analyses are based on multimorbidity clusters from a previous study and combinations of three diseases (triads) identified by observed/expected ratios ≥ 2 and prevalence rates ≥ 1%. In order to visualise a disease network, an edgelist was
Discover how segmentation & cluster analysis can benefit market research for Major Retailers and how Fuel Cycle can help with these techniques today.
Cluster analysis and multidimensional scaling[edit]. "For some multivariate techniques such as multidimensional scaling and ... Principal components analysis[edit]. In principal components analysis, "Variables measured on different scales or on a common ... Everitt, Brian; Hothorn, Torsten J (2011), An Introduction to Applied Multivariate Analysis with R, Springer, ISBN 978- ... cluster analysis, the concept of distance between the units in the data is often of considerable interest and importance … When ...
"Cluster analysis". March 2, 2011.. Retrieved from "https://en.wikipedia.org/w/index.php?title=Euclidean_distance&oldid= ... since it allows convex analysis to be used. Since squaring is a monotonic function of non-negative values, minimizing the SED ... a standard approach to regression analysis. The corresponding loss function is the squared error loss (SEL), and places ...
Cluster analysis: clustering points in the plane,[20] single-linkage clustering (a method of hierarchical clustering),[21] ... Gower, J. C.; Ross, G. J. S. (1969). "Minimum Spanning Trees and Single Linkage Cluster Analysis". Journal of the Royal ... graph-theoretic clustering,[22] and clustering gene expression data.[23]. *Constructing trees for broadcasting in computer ... Asano, T.; Bhattacharya, B.; Keil, M.; Yao, F. (1988). Clustering algorithms based on minimum and maximum spanning trees. ...
Cluster analysis[edit]. Main article: Cluster analysis. In cluster analysis, the k-means algorithm can be used to partition the ... that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in ... from the cluster S. n. {\displaystyle S_{n}}. to the cluster S. m. {\displaystyle S_{m}}. as soon as an x. ,. n. ,. m. {\ ... Principal component analysis[edit]. The relaxed solution of k. -means clustering, specified by the cluster indicators, is given ...
m Cluster analysis‎; 12:40 . . (+20)‎ . . ‎. MartinLjungqvist. (talk , contribs)‎ (Adding wiki link for Raymond Cattell) ... Independent component analysis‎; 18:42 . . (+1)‎ . . ‎. 129.69.118.87. (talk)‎ (fix typo). *(diff , hist) . . Temporal ...
DoBeS cluster. analysis. Miller (2011). analysis[20] bilabial. clicks. dental. clicks. lateral. clicks. alveolar. clicks. ... West ǃXoon has 164 consonants in a strict unit analysis, including 111 clicks in 23 series, which under a cluster analysis ... The DoBeS project takes Traill's cluster analysis to mean that only the twenty tenuis, voiced, nasal, and voiceless nasal ... Taa may have as few as 83 click sounds, if the more complex clicks are analyzed as clusters. Given the intricate clusters ...
Lekic, V.; Cottaar, S.; Dziewonski, A. & Romanowicz, B. (2012). "Cluster analysis of global lower mantle". Earth and Planetary ... The resulting motion forms small clusters of small plumes right above the core-mantle boundary that combine to form larger ... The boundaries of these features appear fairly consistent across models when applying objective k-means clustering. The global ...
Cluster Analysis. Beverly Hills, CA: Sage Publications. ISBN 0-8039-2376-7. Aldenderfer, Mark S. (1993). "Ritual, Hierarchy, ...
"Bread loves Health - Pågen". Cluster analysis. Øresund Food Network. 21 January 2008. Retrieved 2010-02-05. "The Florentine ...
Neighbor-joining Cluster analysis Single-linkage clustering Complete-linkage clustering Hierarchical clustering Models of DNA ... the nearest two clusters are combined into a higher-level cluster. The distance between any two clusters A {\displaystyle {\ ... Everitt, B. S.; Landau, S.; Leese, M. (2001). Cluster Analysis. 4th Edition. London: Arnold. p. 62-64. Legendre P, Legendre L ( ... Complete linkage clustering avoids a drawback of the alternative single linkage clustering method - the so-called chaining ...
Cluster Analysis • Principal Components • Factor Models C39 Other C4 Econometric and Statistical Methods: Special Topics C40 ... Oceania O1 Economic Development O10 General O11 Macroeconomic Analyses of Economic Development O12 Microeconomic Analyses of ... Prices Q12 Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets Q13 Agricultural Markets and Marketing • ... Spatial Production Analysis, and Firm Location R30 General R31 Housing Supply and Markets R32 Other Spatial Production and ...
Gene-cluster analysis in chloroplast genomics. Trends Genet. 1999. *Durnford DG, Deane JA, Tan S, McFadden GI, Gantt E, et al. ... Complete sequence and analysis of the plastid genome of the unicellular red alga Cyanidioschyzon merolae. DNA Res. 2003 ... Germot a, Philippe H. Critical analysis of eukaryotic phylogeny: a case study based on the HSP70 family. J Eukaryot Microbiol. ... Phylogenomic analysis supports the monophyly of cryptophytes and haptophytes and the association of Rhizaria with ...
Cluster analysis statistics for points, zones or lines. CrimeStat has a range of routines available for cluster identification: ... Journey-to-crime analysis for modeling the likely origin of a serial offender based on the location of prior events committed ... 2009). "Spatial analysis of falls in an urban community of Hong Kong", International Journal of Health Geography, 17:8-14 ... Correlated Walk Analysis, based on random walk theory, for modeling the sequential behavior of a serial offender in space and ...
Bianchi, R., Schonfeld, I. S., & Laurent, E. (2014). "Is burnout separable from depression in cluster analysis? A longitudinal ... Alarcon, G.; Eschleman, K. J.; Bowling, N. A. (2009). "Relationships between personality variables and burnout: A meta-analysis ... A multitrait-multimethod analysis". European Journal of Psychological Assessment. 18 (1): 296-307. doi:10.1037/a0037726. PMID ...
Bioinformatic analysis identified four methyltransferases within the cluster. Bioinformatics suggest that btmB, is an O- ... The presence of a follower peptide was identified by bioinformatic analysis of the bottromycin biosynthetic cluster. The ... Crone, W. J. K.; F. J. Leeper; A. W. Truman (2012). "Identification and characterisation of the gene cluster for the anti-MRSA ... As such, there are still three predicted radical SAM dependent enzymes in the bottromycin D biosynthetic cluster: bstC, bstF, ...
... normally refers to cluster analysis. Classification and clustering are examples of the more general problem of pattern ... Binder, D. A. (1978). "Bayesian cluster analysis". Biometrika. 65: 31-38. doi:10.1093/biomet/65.1.31. Binder, David A. (1981 ... ISBN 0-471-30845-5 (p. 83-86) Rao, C.R. (1952) Advanced Statistical Methods in Multivariate Analysis, Wiley. (Section 9c) ... Computer vision Medical imaging and medical image analysis Optical character recognition Video tracking Drug discovery and ...
Functionpoint cluster analysis. Systematic Zoology, September 1973, Vol. 22, No. 3, pp. 295-301. Katz is fluent in written and ... Jeffrey Owen Katz (born 1950) is an American scientist best known for his pivotal contribution to the field of factor analysis ... Katz, Jeffrey Owen, and McCormick, Donna L. (May 1998). "Using Barrier Stops in Exit Strategies." Technical Analysis of Stocks ... Katz, Jeffrey Owen, and McCormick, Donna L. (November 1997). "Adding the Human Element to Neural Nets." Technical Analysis of ...
The Democracy Cluster Classification Index. Political Analysis. 2013-07-01, 21 (3): 334-349 [2014-03-24]. ISSN 1047-1987. doi: ... Gugiu&Centellas使用集群分類法,將五種常用民主指標(含DD資料集、政體資料集、自由之家
"Sets of statistical cluster points and ℐ-cluster points". Real Analysis Exchange. 30 (2): 565-580. MR 2177419. Pavel Kostyrko; ... Tibor Šalát; S. James Taylor; János T. Tóth (1998). "Radii of Convergence of Power Series". Real Analysis Exchange. 24 (1): 263 ... Real Analysis Exchange. 21 (2): 725-731. MR 1407285. M. Dindoš; T. Šalát; V. Toma (2003). "Statistical Convergence of Infinite ... and Doctor of Mathematics who specialized in number theory and real analysis. He was the author and co-author of undergraduate ...
Cluster analysis for applications. New York: Academic Press. Mendel, Gregor (1866). "Versuche über Pflanzen Hybriden". ... Such coefficients are used particularly in regression analysis. A standardized version of regression analysis is path analysis ... analysis is more informative, and that a "Fisher" analysis can always be constructed from it. The opposite conversion is not ... Path analysis demonstrates that these are tantamount to the same thing. Arising from this background, the inbreeding ...
H. Charles Romesburg (1984). Cluster Analysis for Researchers. Belmont, California: Lifetime Learning Publications. p. 149. ... The technique is also used to measure cohesion within clusters in the field of data mining. The term cosine distance is often ...
2004). "PMMC cluster analysis". Computer Modeling in Engineering & Sciences. 5 (2): 171-188. doi:10.3970/cmes.2004.005.171. ... Dermatology Osteoporosis analysis. Radiology Chromosome classification. Cytogenetics Seed Analysis. Metrology In the field of ... Measurements based on shape analysis (surface area, perimeter, volume, elongation, compactness, etc.) and texture analysis (e.g ... Image analysis also helps to study composite polymers strengthen by glass fiber, and to measure the impact of the size of micro ...
"Manufacturing Cluster Analysis" (PDF). Oklahoma Chamber of Commerce. 2005. Archived from the original (PDF) on August 8, 2007. ... "GDP by state current dollars". US Bureau of Economic Analysis. Retrieved September 27, 2011. "Per capita real GDP by state". US ... Bureau of Economic Analysis. Retrieved September 27, 2011. Snead, Mark (2006). "Outlook Update-OKC GM Plant Closing" (PDF). ...
H. Charles Romesburg (1984). Cluster Analysis for Researchers. Belmont, California: Lifetime Learning Publications. p. 149. ...
Curve fitting allows an analysis of the global (macro) structure as well as the temporal dynamics of clusters and switches, and ... other analyses such as number of repetitions, number and length of clusters of words from the same semantic or phonemic ... the figure on the right shows a hierarchical clustering analysis of animal semantic fluency data from 55 British schoolchildren ... Furthermore, by means of curve fitting, temporal clusters, switches,[6][7] and the initial slope can be determined. Whereas the ...
According to Scopus the most cited ones are: Ramoni M.F.; Sebastiani P.; Kohane I.S. "Cluster analysis of gene expression ... "Human longevity and common variations in the LMNA gene: a meta-analysis". Aging Cell. 11: 475-481. doi:10.1111/j.1474-9726.2012 ... "Meta-analysis of genetic variants associated with human exceptional longevity". Aging. 5: 653-61. doi:10.18632/aging.100594. ...
With L. Kaufman he coined the word medoid when proposing the k-medoids method for cluster analysis, also known as Partitioning ... Kaufman, L.; Rousseeuw, P.J. (1987). "Clustering by means of Medoids". Statistical Data Analysis Based on the L1-Norm and ... Kaufman, Leonard; Rousseeuw, Peter J. (1990). Finding groups in data : an introduction to cluster analysis (3. print. ed.). New ... His silhouette display shows the result of a cluster analysis, and the resulting index is often used to select the number of ...
"Manufacturing Cluster Analysis" (pdf). Oklahoma Chamber of Commerce. 2005. Arkiveret (PDF) fra originalen 8. august 2007. ...
An answer by cluster analysis". Journal of Psychosomatic Research. 74 (1): 6-11. doi:10.1016/j.jpsychores.2012.09.013. ISSN ... Some have proposed to group symptoms into clusters or into one general functional somatic disorder given the finding of ... A systematic review with meta-analysis". Arthritis Care & Research. 63 (6): 808-820. doi:10.1002/acr.20328. ISSN 2151-464X. ... A Systematic Review and Meta-Analysis". Psychosomatic Medicine. 76 (1): 2-11. doi:10.1097/PSY.0000000000000010. ISSN 0033-3174 ...
Abu-Jamous, Basel; Fa, Rui; Nandi, Asoke K. (2015-04-16). Integrative Cluster Analysis in Bioinformatics. John Wiley & Sons. p ...
Deletions that remove the entire HOXD gene cluster or the 5' end of this cluster have been associated with severe limb and ... 2002). "Complete mutation analysis panel of the 39 human HOX genes". Teratology. 65 (2): 50-62. doi:10.1002/tera.10009. PMID ... Limongi MZ, Pelliccia F, Gaddini L, Rocchi A (2000). "Clustering of two fragile sites and seven homeobox genes in human ... Mammals possess four similar homeobox gene clusters, HOXA, HOXB, HOXC and HOXD, located on different chromosomes, consisting of ...
The small world attribute refers to the many loosely connected nodes, non-random dense clustering of a few nodes (i.e., trophic ... Published examples that are used in meta analysis are of variable quality with omissions. However, the number of empirical ... such as those used in other kinds of network analysis (e.g., graph theory), to study emergent patterns and properties shared ... Ecologists identify feeding relations and organize species into trophic species through extensive gut content analysis of ...
Ernst, E. (2008). "Placebo and other Non-specific Effects". In Ernst, E. (ed.). Healing, Hype, or Harm? A Critical Analysis of ... "A cluster of lead poisoning among consumers of Ayurvedic medicine". International Journal of Occupational and Environmental ... An analysis of the conclusions of only the 145 Cochrane reviews was done by two readers. In 83% of the cases, the readers ... An analysis of trends in the criticism of complementary and alternative medicine (CAM) in five prestigious American medical ...
... and lawrencium by the relativistic coupled-cluster method". Phys. Rev. A. 52 (1): 291-296. Bibcode:1995PhRvA..52..291E. doi: ... and published his completed analysis in 1794;[3] in 1797, the new oxide was named yttria.[4] In the decades after French ... "Formation of Yttrium Oxide Clusters Using Pulsed Laser Vaporization". Bull. Korean Chem. Soc. 26 (2): 345-348. doi:10.5012/ ...
At the core of each bundle are clusters of two distinct types of conducting cells: Xylem. Cells that bring water and minerals ... Analyses of vein patterns often fall into consideration of the vein orders, primary vein type, secondary vein type (major veins ... A Fractal Analysis Approach to Tune Mechanical Rigidity of Scaffolding Matrix in Thin Films". Advanced Materials Research. 1141 ... "Leaf Vascular Systems in C3 and C4 Grasses: A Two-dimensional Analysis". Annals of Botany. 97 (4): 611-621. doi:10.1093/aob/ ...
... further evidence that modern GABAA receptor gene clusters are derived from an ancestral cluster". Genomics. 26 (3): 580-586. ... "Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences". Proc. Natl. Acad. Sci. U.S.A ... to human chromosome 4 defines an alpha 2-alpha 4-beta 1-gamma 1 gene cluster: ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... This ensures that subsequent user errors cannot inadvertently perform meaningless analyses (for example correlation analysis ... Mosteller, Frederick (1977). Data analysis and regression : a second course in statistics. Reading, Mass: Addison-Wesley Pub. ... Cliff, N. (1996). Ordinal Methods for Behavioral Data Analysis. Mahwah, NJ: Lawrence Erlbaum. ISBN 0-8058-1333-0 ...
In primitive red algae, the chloroplast DNA nucleoids are clustered in the center of a chloroplast, while in green plants and ... "Evolutionary analysis of Arabidopsis, cyanobacterial, and chloroplast genomes reveals plastid phylogeny and thousands of ...
In contrast, when the B cell lacked this asymmetric protein cluster, it was killed only 40% of the time.[36][37] ... "Treatment of neuromyelitis optica with rituximab: retrospective analysis of 25 patients". Arch Neurol. 65 (11): 1443-1448. doi ...
JEL: Q11 - Aggregate Supply and Demand Analysis; Prices. JEL: Q12 - Micro Analysis of Farm Firms, Farm Households, and Farm ... C38 - Métodos de classificação; Análise de clusters; Análise de fatores. *C39 - Outros ... JEL: I11 - Analysis of Health Care Markets. JEL: I12 - Health Production: Nutrition, Mortality, Morbidity, Substance abuse and ... JEL: R2 - Household Analysis JEL: R20 - Geral. JEL: R21 - Housing Demand. JEL: R22 - Outros Demand. JEL: R23 - Regional ...
2007). "Proteomic analysis of human very low-density lipoprotein by two-dimensional gel electrophoresis and MALDI-TOF/TOF". ... 2002). "Regulated expression of the apolipoprotein E/C-I/C-IV/C-II gene cluster in murine and human macrophages. A critical ... "Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences". Proc. Natl. Acad. Sci. U.S.A ... "Large-scale candidate gene analysis of spontaneous clearance of hepatitis C virus". J. Infect. Dis. 201 (9): 1371-80. doi ...
In contrast, when the B cell lacked this asymmetric protein cluster, it was killed only 40% of the time.[37][38] ... Kriston, Levente (2009). "Challenges in Reporting Meta-analyses of Diagnostic Accuracy Studies". Annals of Internal Medicine. ... "Treatment of neuromyelitis optica with rituximab: retrospective analysis of 25 patients". Arch Neurol. 65 (11): 1443-1448. doi ...
3. Modularity of a graph clustering, the difference of the number of cross-cluster edges from its expected value.. monotone. A ... 2. Power graph analysis is a method for analyzing complex networks by identifying cliques, bicliques, and stars within the ... A branch-decomposition of G is a hierarchical clustering of the edges of G, represented by an unrooted binary tree with its ...
The genome contains multiple clusters of genes encoding proteins essential for survival in a nutrient-poor habitat. Included ... "The diversity and evolution of cell cycle regulation in alpha-proteobacteria: A comparative genomic analysis". BMC Systems ...
Consonant clusters include pr, tr and kr. Like most mainland languages, Tasmanian languages lacked sibilants (which is apparent ... of the Australian Mainland languages as a guide to Palawa phonology without undertaking an adequate comparative analysis of the ... The sequence 'tr' is treated as a consonant cluster, when it was presumably a postalveolar affricate closer to English j or ch. ...
The check-in area is located in the public area at Level 1 and houses 118 counters organised in eight clusters, called check-in ... Central demand: an analysis of BER capacity and airplane noise]. tagesspiegel. Retrieved 22 October 2017.. ... new TUV analysis, diverging passenger number predictions- further lack of time and as well whether Tegel will carry on]. ...
His most important contributions are the database index structures R*-tree, X-tree and IQ-Tree, the cluster analysis algorithms ... His current research is focused around correlation clustering, high-dimensional data indexing and analysis, spatial data mining ... He was also awarded the 2015 ACM SIGKDD Innovation Award for his contributions to data mining in clustering, outlier detection ... E. Rahm, A. Thor (2005). "Citation analysis of database publications" (PDF). SIGMOD Record. Association for Computing Machinery ...
This analysis yielded the conclusion that, in their culture of Wistar rat neocortical cells, the AWSR has long rise and fall ... It follows that each electrode in the array services a large cluster of neurons and cannot provide resolute information ... Moreover, chemical analysis of the neurons and their environment is more easily accomplished than in an in vivo setting. ... Stegenga J, Feber JL, Marani E, Rutten WL (2008). "Analysis of Cultured Neuronal Networks Using Intraburst Firing ...
Cluster analysis. *Classification. *Structural equation model *Factor analysis. *Multivariate distributions *Elliptical ... Kruskal-Wallis one-way analysis of variance. Notes[edit]. *^ a b Mann, Henry B.; Whitney, Donald R. (1947). "On a Test of ... Zar, Jerrold H. (1998). Biostatistical Analysis. New Jersey: Prentice Hall International, INC. p. 147. ISBN 0-13-082390-2.. ... A thorough analysis of the statistic, which included a recurrence allowing the computation of tail probabilities for arbitrary ...
Example of a VBM analysis on patients who experience cluster headaches.. Voxel-based morphometry is a computational approach to ... The usual approach for statistical analysis is mass-univariate (analysis of each voxel separately), but pattern recognition may ... Tutorial: A Critical Analysis of Voxel Based Morphometry (VBM). *Voxel-Based Morphometry Should Not Be Used with Imperfectly ... Actual statistical analysis by the general linear model, i.e., statistical parametric mapping. ...
Interpretation of concentrations determined by analysis is easy only when a nutrient occurs in excessively low or occasionally ... giving the appearance of a cluster of berries. ... Phylogeny of the Pinophyta based on cladistic analysis of ...
"Ohio State University SAT Score Analysis (New 1600 SAT)". prepscholar.com. PrepScholar. Retrieved April 18, 2019.. ... Ohio State operates 41 on-campus residence halls divided into three geographic clusters: South Campus (site of the university's ... Center for Urban and Regional Analysis and Ohio Agricultural Research and Development Center. ...
The full syndrome now known as Lyme disease was not recognized until a cluster of cases originally thought to be juvenile ... Lyme disease fact sheet Analysis of CDC data on VoxHealth. Retrieved on 2013-30-1 ... Demonstration by lumbar puncture and CSF analysis of pleocytosis and intrathecal antibody production are required for definite ... American guidelines consider CSF analysis optional when symptoms appear to be confined to the peripheral nervous system (PNS), ...
4 ions.[66] The chemistry of alkali metal germanides, involving the germanide ion Ge4− and other cluster (Zintl) ions such as ... "Chemical risk analysis: a practical handbook. p. 215. ISBN 978-1-903996-65-2. .. ... 3 cluster, composed of three regular octahedra where each octahedron is connected to both of the others by one face each. All ... 2 cluster, composed of two regular octahedra connected to each other by one face ...
In this analysis, it is imperative that data from at least 50 sample plots is considered. The number of individuals present in ... Individuals might be clustered together in an area due to social factors such as selfish herds and family groups. Organisms ... However, many researchers believe that species distribution models based on statistical analysis, without including ecological ... point pattern analysis of the burrows of great gerbils in Kazakhstan". Journal of Biogeography. 42 (7): 1281-1292. doi:10.1111/ ...
Such diseases clustered around the tracks of railways.[54]. Soil and water supplyEdit. The soil profile in Bengal differs ... However, FEE analyses do not consider shortage the main factor,[321] while FAD-oriented scholars such as Bowbrick (1986) hold ... More recent analyses often stress political factors.[BA] Discussions of the government's role split into two broad camps: those ... Sen, Amartya (1981b). "Ingredients of Famine Analysis: Availability and Entitlements". The Quarterly Journal of Economics. 96 ( ...
Centre for Analysis of Strategies and Technologies. 2010. p. 19. Archived (PDF) from the original on 10 September 2018. ... totaling 22 sensor clusters installed at several points on the turret. A new 125/EPpSV-97 APFSDS round was developed for use ...
2, pp.85-93 and "Atomic Structure Analysis of Pd Nano-Cluster in Nano-Composite Pd⁄ZrO2 Absorbing Deuterium" - Journal of High ... Wilson, R.H.; Bray, J.W.; Kosky, P.G.; Vakil, H.B.; Will, F.G. (1992), "Analysis of experiments on the calorimetry of LiOD-D2O ... Cold fusion reports continued to be published in a small cluster of specialized journals like Journal of Electroanalytical ... Miskelly, GM; Heben MJ; Kumar A; Penner RM; Sailor MJ; Lewis NL (1989), "Analysis of the Published Calorimetric Evidence for ...
There are several clusters, the first being albumin, the second being the alpha, the third beta and the fourth gamma ( ... Kolarich D, Weber A, Turecek PL, Schwarz HP, Altmann F (June 2006). "Comprehensive glyco-proteomic analysis of human alpha1- ... Mahr AD, Neogi T, Merkel PA (2006). "Epidemiology of Wegener's granulomatosis: Lessons from descriptive studies and analyses of ... and glycoproteomic analyses of alpha(1)-proteinase inhibitor products used for replacement therapy". Transfusion. 46 (11): 1959 ...
A re-analysis and review,url=,journal=Breastfeeding Review,language=en,volume=18,issue=2,pages=25-32,pmid=20879657,via=}},/ref ... and Echinacea in Three Main Immune Interactive Clusters (Physical Barriers, Innate and Adaptive Immunity) Involved during an ... Sequencing and Analyses of All Known Human Rhinovirus Genomes Reveals Structure and Evolution,url=,journal=Science,language=en, ... An Individual Patient Data Meta-Analysis,url=,journal=Open Forum Infect Diseases,language=en,volume=4,issue=2,pages=ofx059,doi= ...
Cluster Analysis BIBLIOGRAPHY [1] Quantitative social science [2] often involves measurements of several variables for a number ... of cases (individuals or subjects). Searching for groupings, or clusters, is an important exploratory technique. ... Hierarchical cluster analysis methods form clusters in sequence, either by amalgamation of units into clusters and clusters ... John Hartigans Clustering Algorithms (1975) did much to stimulate interest in cluster analysis. Geoff McLachlan and David Peel ...
The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy ,i ,c,/i,- ... The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number ... However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this ... It overcomes the drawback of FCM algorithm which needs to define the cluster number ,svg style=vertical-align:-0.1638pt;width: ...
The objective of the clustering algorithm is to divide the given dataset (a set of points ... - Selection from R: Data Analysis ... Cluster Analysis Clustering is defined as an unsupervised classification of a dataset. ... Clustering is defined as an unsupervised classification of a dataset. The objective of the clustering algorithm is to divide ... R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia ...
Cluster Analysis. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 ... A final analysis that wed like to do with these data is whats called a cluster ... The middle three clusters, these are kind of one tool clusters, the analyst, ... This is a cluster I wouldnt have known existed. I kind of discovered it over the years when I would do the debrief of students ...
Hard clustering: each object belongs to a cluster or not. *Soft clustering (also: fuzzy clustering): each object belongs to ... Main category: Cluster analysis algorithms. Clustering algorithms can be categorized based on their cluster model, as listed ... Cluster tendencyEdit. To measure cluster tendency is to measure to what degree clusters exist in the data to be clustered, and ... Overlapping clustering (also: alternative clustering, multi-view clustering): objects may belong to more than one cluster; ...
... cluster A), the cell cycle (cluster B), the immediate-early response (cluster C), signaling and angiogenesis (cluster D), and ... When the clustering analysis described here is applied to all of the approximately 6,200 genes of S. cerevisiae, the clusters ... Cluster analysis and display of genome-wide expression patterns. Michael B. Eisen, Paul T. Spellman, Patrick O. Brown, David ... Cluster analysis and display of genome-wide expression patterns. Michael B. Eisen, Paul T. Spellman, Patrick O. Brown, David ...
Fuzzy Cluster Weather Data Validity Index Cluster Validity Index Fuzzy Cluster Analysis These keywords were added by machine ... Liu Z., George R. (2005) Mining Weather Data Using Fuzzy Cluster Analysis. In: Petry F.E., Robinson V.B., Cobb M.A. (eds) Fuzzy ... Forgy, E., Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of Classifications. Biometry, 1965. 21(785 ... It introduces an unsupervised fuzzy clustering algorithm, based on the fuzzy KMeans and defines a cluster validity index which ...
ANALYSIS: Future turning bright for Montreals aerospace cluster. ANALYSIS: Future turning bright for Montreals aerospace ... Montreals aerospace industry cluster has good reason to celebrate. First came Bombardiers recently announced CSeries orders, ... ANALYSIS: Puerto Rico advances effort to establish itself as hub a day ago ... Registration gives you instant free access to FlightGlobals news, in-depth analysis, insight and opinion from our global team ...
Discover the basic concepts of cluster analysis, and then study a set of typical ... Enroll for free. ... learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in ... Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and ... TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING. ... Cluster AnalysisData Clustering AlgorithmsK-Means Clustering ...
... From. Matt Loke ,[email protected],. To. ,[email protected],. Subject. RE ... 2-stages cluster analysis. Date. Tue, 21 Sep 2010 06:10:13 +0200. Thanks for your help. Nevertheless, I need you to explain me ... Thanks, Matt , Date: Tue, 14 Sep 2010 10:19:14 -0500 , Subject: Re: st: 2-stages cluster analysis , From: [email protected] , ... Re: st: 2-stages cluster analysis *From: Stas Kolenikov ,[email protected], ...
... visualisation of cluster analysis results and cluster validation results. Visualisation is essential for a better understanding ... Instrumental Neutron Activation Analysis Hierarchical Cluster Analysis Confusion Matrix Individual Cluster Cluster Validation ... Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis,52, 258-271.Google ... We present some methods for (multivariate) visualisation of cluster analysis results and cluster validation results. ...
Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively ... In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of ... Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key ... An absolute must for those who really care about the mathematical-statistical foundations of probabilistic cluster analysis and ...
What the algorithm does is finds the cluster (centroid) positions that minimize the distances to all points in ... - Selection ... K-means clustering The K-means algorithm is also referred to as vector quantization. ... What the algorithm does is finds the cluster (centroid) positions that minimize the distances to all points in the cluster. ... Mastering Python Data Analysis by Luiz Felipe Martins, Magnus Vilhelm Persson. Stay ahead with the worlds most comprehensive ...
Consequently, the clustering results generated by the DAC-based cluster-ing algorithm (DACA) are robust to the outlier distur- ... In this paper, we present a discriminant analysis based graph partitioning criterion (DAC), which is de-signed to effectively ... Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of DACA. 1 ... Many existing spectral clustering algorithms share a conventional graph partitioning criterion: normal-ized cuts (NC). However ...
The LOVE clustering approach is a rigorous, adaptable, and scalable latent model-based statistical method that can be used in ... The LOVE clustering approach is a rigorous, adaptable, and scalable latent model-based statistical method that can be used in ... In this study, LOVE generates meaningful clusters from datasets spanning from a large range of biological areas and is used to ... In addition, the algorithmic technique demonstrated power in generating both overlapping and non-overlapping clusters. Such ...
Learn the basics of how to conduct cluster analysis, and how this process can help your business. ... Cluster analysis is a statistical method used to group similar objects into respective categories by identifying trends and ... The Different Types of Cluster Analysis. There are three primary methods used to perform cluster analysis: Hierarchical Cluster ... Putting Clustering into Context. Its easy to overthink cluster analysis, but our brains naturally cluster data on a regular ...
Browse the latest Cluster Analysis jobs on Guru. Apply online for freelance Cluster Analysis jobs. Be the first to send a quote ... Cluster Analysis Jobs. We found 10 freelance jobs online. Send a Quote to get hired.. ... Implement K-means clustering algorithm using OpenMP and apply it to the clustering of the medical data. Serial code is ... We have our app deployed in a Rancher cluster with and haproxy ingress balancing the load to our app nodes. both haproxy and ...
The clustering methods have to assume some cluster relationship among the data objects that they are applies on. Similarity ... The clustering methods have to assume some cluster relationship among the data objects that they are applies on. Similarity ... In this paper, the authors introduce a novel multi viewpoint based similarity measure and two related clustering methods. The ... which is the origin while the latter utilizes many different viewpoints which are objects assumed to not be in the same cluster ...
Assessment of risks to the stability of the Canadian financial system, including risks stemming from the COVID-19 pandemic - Deputy Governor Toni Gravelle of the Bank of Canada speaks before the Autorité des marchés financiers. (14:00 (ET) approx.). ...
CLUSTERING TECHNIQUES Clustering or Cluster analysis is defined as the process of organizing objects into groups whose members ... CLUSTERING TECHNIQUES. Clustering or Cluster analysis is defined as the process of organizing objects into groups whose members ... Clustering Or Cluster Analysis Is Defined As The Process Of Organizing Objects. 1772 Words8 Pages ... Clustering Or Cluster Analysis Is Defined As The Process Of Organizing Objects ...
The problem of clustering multivariate observations is viewed as the replacement of a set of vectors with a set of labels and ... A general criterion for clustering is derived as a measure of representation error. Some special cases are derived by ...
Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is ... We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and ... How many clusters are there?, Which clustering method should be used? and \How should outliers be handled?. We outline a ... systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, ...
... is the unit of analysis.2 A simple approach is to construct a summary statistic for each cluster and then analyse these summary ... Analysis of a trial randomised in clusters. BMJ 1998; 316 doi: https://doi.org/10.1136/bmj.316.7124.54 (Published 03 January ... A cluster randomised study is one where a group of subjects are randomised to the same treatment together-for example, when ... The idea is similar to the analysis of repeated measurements on the same subject, where we construct a single summary … ...
The cluster of technical support surrounding 12100 in EURAUD suggests that the level will be difficult to break. Other crosses ... FOREX Analysis: EURAUD Nears Cluster of Technical Levels. 2012-11-13 20:16:00 Jamie Saettele, CMT, Sr. Technical Strategist ... Analysis_EURAUD_Nears_Cluster_of_Technical_Levels.html; }; (function() { var d = document, s = d.createElement(script); s. ... FOREX Technical Analysis Observations: The AUDNZD has reversed from corrective channel resistance and confirmed an inside day ...
Clustering or cluster analysis is a type of data analysis. The analyst groups objects so that objects in the same group (called ... a cluster) are more similar to each other than to objects in other groups (clusters) in some way. This is a common task in data ... Retrieved from "https://simple.wikipedia.org/w/index.php?title=Cluster_analysis&oldid=6123443" ...
For details, see H.-J. Mucha, U. Simon, R. Br ggemann (2002): Model-based Cluster Analysis Applied to Flow Cytometry Data of ... clusters). Objects that are similar one to another form a cluster, whereas dissimilar ones belong to different clusters. Here ... is the sample cross-product matrix for the kth cluster. Criterion (1) can be expressed without using mean vectors of clusters k ... Fig 5. Principal component analysis plot of the result of core-based clustering of 613 observations (Roman bricks).. ...
Analysis. In a standard parallel trial the intervention is allocated to some clusters and not to others and analysis compares ... However, if substantial cluster-level effects are present (that is, larger intra-cluster correlations) or the clusters are ... The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting BMJ 2015; 350 :h391 ... Other options for analysis include using within cluster comparisons only (although this does not adjust for any confounding ...
We explore an approach to possibilistic fuzzy clustering that avoids a severe drawback of the conventional approach, namely ...
The quality of most of the websites dedicated to cluster headache is mediocre, and although there are some excellent cluster ... The technical quality of the cluster headache information was analyzed based on content specific to cluster headache. The final ... Websites providing cluster headache information were determined on the search engine MetaCrawler and classified as either ... Keywords: Flesch-Kincaid Grade Level Readability Score; World Wide Web; cluster headache; internet; websites ...
Spatial Clustering Analysis. To understand where on the landscape clustering of stroke hospitalization rates may be occurring, ... All spatial clustering analysis was implemented in GeoDA software, statistical analysis was performed using JMP 13 (SAS ... Table 1: Comparison of persistent high-rate clusters with persistent low-rate clusters and transitional clusters. ... Cluster analysis conducted at the national scale using the Gi. statistic identified areas where the stoke hospitalization rate ...
  • The K -means algorithm assigns each case to the cluster having the nearest centroid. (encyclopedia.com)
  • The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c -means) algorithm. (hindawi.com)
  • A weighted bipartite network is first constructed by using of membership matrix and centers of each clusters obtained by FCM algorithm. (hindawi.com)
  • No matter what, we take crisp partitioning method or fuzzy clustering algorithm to separate data set into groups, the generated partitions may not reflect the desired clustering of the data because of inappropriate choice of algorithmic parameters. (hindawi.com)
  • The objective of the clustering algorithm is to divide the given dataset (a set of points or objects) into groups of data instances or objects (or points) with distance or probabilistic measures. (oreilly.com)
  • Cluster analysis itself is not one specific algorithm , but the general task to be solved. (wikipedia.org)
  • The appropriate clustering algorithm and parameter settings (including parameters such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. (wikipedia.org)
  • for example, the k-means algorithm represents each cluster by a single mean vector. (wikipedia.org)
  • clusters are modeled using statistical distributions, such as multivariate normal distributions used by the expectation-maximization algorithm . (wikipedia.org)
  • Relaxations of the complete connectivity requirement (a fraction of the edges can be missing) are known as quasi-cliques, as in the HCS clustering algorithm . (wikipedia.org)
  • It proposes the extension of the fuzzy K-Means clustering algorithm to account for the spatio-temporal nature of weather data. (springer.com)
  • It introduces an unsupervised fuzzy clustering algorithm, based on the fuzzy KMeans and defines a cluster validity index which is used to determine an optimal number of clusters. (springer.com)
  • A New Shared Nearest Neighbor Clustering Algorithm and Its Applications. (springer.com)
  • What the algorithm does is finds the cluster (centroid) positions that minimize the distances to all points in the cluster. (oreilly.com)
  • On Spectral Clustering: Analysis and an Algorithm - Ng, Jordan, et al. (psu.edu)
  • 7] present a min-max cut algorithm for graph partitioning and data clustering. (psu.edu)
  • 8]-=- present a clustering algorithm based on KMeans after the spectral relaxation. (psu.edu)
  • This method uses a cluster algorithm to identify groupings by performing pre-clustering first, and then performing hierarchical methods. (surveygizmo.com)
  • Implement K-means clustering algorithm using OpenMP and apply it to the clustering of the medical data. (guru.com)
  • Hierarchical clustering, which groups states on the basis of their mutual similarities, is a top-down approach in which all states are initially included in the same cluster, and the algorithm splits the states based on differences down the hierarchy. (cdc.gov)
  • The algorithm ends when each state is in its own cluster. (cdc.gov)
  • Selection of a clustering algorithm, to ensure relevant and strong correlations between datasets. (computerweekly.com)
  • Deciding heuristics for the clustering algorithm, to pinpoint and group "malicious" Internet traffic. (computerweekly.com)
  • The clustering algorithm then classifies traffic, after which heuristics are applied. (computerweekly.com)
  • ACDC: An Algorithm for Comprehension-Driven Clustering. (program-transformation.org)
  • Intensity and seasonality of cyclones, though not used by the clustering algorithm, are both highly stratified from cluster to cluster. (columbia.edu)
  • We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. (frontiersin.org)
  • We explain how the problem can be expressed as a quadratic unconstrained binary optimization problem and show that the introduced quantum-assisted clustering algorithm is, regarding accuracy, equivalent to commonly used classical clustering algorithms. (frontiersin.org)
  • Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical clustering algorithm. (frontiersin.org)
  • The introduced quantum-assisted clustering algorithm falls into that category, as it utilizes the topological properties of the chip for assigning clusters. (frontiersin.org)
  • We propose a K-mean based algorithm in which gene expression levels fluctuate in parallel will be clustered together. (spie.org)
  • Under the assumption that the views are uncorrelated given the cluster label, we show that the separation conditions required for the algorithm to be successful are significantly weaker than prior results in the literature. (videolectures.net)
  • The output of this algorithm is a flat structure of clusters. (igi-global.com)
  • The following algorithm is an agglomerative scheme that erases rows and columns in a proximity matrix as old clusters are merged into new ones. (wikipedia.org)
  • There are two broad types of clustering algorithms: hierarchical clustering and nonhierarchical clustering (partitioning). (encyclopedia.com)
  • In past decades, a number of clustering algorithms have been proposed, attempting to classify the given data set into groups by using different similarities. (hindawi.com)
  • Generally, these algorithms can be classified into three cases: hard (crisp) cluster, soft (fuzzy) cluster, and possibilistic clustering [ 1 - 3 ]. (hindawi.com)
  • However, these algorithms require the user to specify the number , of clusters which the user does not usually know in advance or may not want to specify. (hindawi.com)
  • It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. (wikipedia.org)
  • The notion of a "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms. (wikipedia.org)
  • However, different researchers employ different cluster models, and for each of these cluster models again different algorithms can be given. (wikipedia.org)
  • The notion of a cluster, as found by different algorithms, varies significantly in its properties. (wikipedia.org)
  • Understanding these "cluster models" is key to understanding the differences between the various algorithms. (wikipedia.org)
  • A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. (pnas.org)
  • Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. (coursera.org)
  • Robust Cluster Analysis and Variable Selection includes all of the important theoretical details, and covers the key probabilistic models, robustness issues, optimization algorithms, validation techniques, and variable selection methods. (routledge.com)
  • This provides you with guidance in how to use clustering methods as well as applicable procedures and algorithms without having to understand their probabilistic fundamentals. (routledge.com)
  • Using clustering algorithms in LegacySystems remodularization. (program-transformation.org)
  • Unfortunately, there has been no rigorous experimentation or evaluation of fuzzy hashing algorithms for malware similarity analysis in the research literature. (usenix.org)
  • In this paper, we perform extensive study of existing fuzzy hashing algorithms with the goal of understanding their applicability in clustering similar malware. (usenix.org)
  • Our experiments indicate that current popular fuzzy hashing algorithms suffer from serious limitations that preclude them from being used in similarity analysis. (usenix.org)
  • Traditional web pages clustering typically uses only the page content (usually the page text) in an appropriate feature vector representation such as bags of words, term frequency/inverse document frequency, etc. and then applies standard clustering algorithms(e.g. k-means, suffix tree, query directed clustering). (techrepublic.com)
  • A number of efficient clustering algorithms developed in recent years address this problem by projecting the data into a lower dimensional subspace, e.g. via Principal Components Analysis (PCA) or random projections, before clustering. (videolectures.net)
  • There are two major categories of clustering algorithms with respect to the output structure: partitional and hierarchical (Romesburg, 1990). (igi-global.com)
  • Hierarchical clustering algorithms produce a hierarchical structure often presented graphically as a dendrogram. (igi-global.com)
  • It is not the purpose of this paper to survey the various methods available to cluster genes on the basis of their expression patterns, but rather to illustrate how such methods can be useful to biologists in the analysis of gene expression data. (pnas.org)
  • Clustering methods can be divided into two general classes, designated supervised and unsupervised clustering ( 4 ). (pnas.org)
  • Although various clustering methods can usefully organize tables of gene expression measurements, the resulting ordered but still massive collection of numbers remains difficult to assimilate. (pnas.org)
  • MacQueen, J.B. Some Methods for Classification and Analysis of Multivariate Observations. (springer.com)
  • deGruijter, J.J. and A.B. McBratney, A Modified Fuzzy K Means for Predictive Classification, in Classification and Related Methods of Data Analysis, H.H. Bock, Editor. (springer.com)
  • Moreover, learn methods for clustering validation and evaluation of clustering quality. (coursera.org)
  • We present some methods for (multivariate) visualisation of cluster analysis results and cluster validation results. (springer.com)
  • The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. (routledge.com)
  • Cluster analysis differs from many other statistical methods due to the fact that it's mostly used when researchers do not have an assumed principle or fact that they are using as the foundation of their research. (surveygizmo.com)
  • Two-step clustering is best for handling larger datasets that would otherwise take too long a time to calculate with strictly hierarchical methods. (surveygizmo.com)
  • The clustering methods have to assume some cluster relationship among the data objects that they are applies on. (techrepublic.com)
  • In this paper, the authors introduce a novel multi viewpoint based similarity measure and two related clustering methods. (techrepublic.com)
  • Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. (psu.edu)
  • However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as \How many clusters are there? (psu.edu)
  • Methods of cluster analysis, classification and multivariate graphics can be used in order to extract hidden knowledge from huge data sets containing numerical and non-numerical information. (wias-berlin.de)
  • In case of big data, the methods based on clustering of cores (representative points, mean vectors) can be recommended. (wias-berlin.de)
  • Partitioning methods start with an initial (random) partition and proceed by exchanging observations between clusters. (wias-berlin.de)
  • The objective of this paper is to address this limitation, by proposing a novel methodological approach in the evaluation of CDPs based on the application of concepts and methods of social network analysis (SNA). (repec.org)
  • Plus it can actually output a single cluster if that's what the data tell you - some of the methods in @Ben's excellent answers won't help you determine whether k=1 is actually best. (stackoverflow.com)
  • The wikipedia article on determining numbers of clusters has a good review of some of these methods. (stackoverflow.com)
  • In particular this allows the checking of the differences and agreements between different methods of analysis. (nih.gov)
  • Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular, shows interesting differences between individual voxel analysis performed with traditional methods. (nih.gov)
  • The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications. (researchandmarkets.com)
  • Although the research considered herein has created a meaningful body of literature, refining both the factor and cluster analysis methods will help to further establish eating patterns as a sound dietary assessment method. (nih.gov)
  • However, statistical methods for the analysis of binary data arising from such designs are not well developed. (nih.gov)
  • Cluster seed selection methods. (sas.com)
  • Reaching across disciplines, Aldenderfer and Blashfield pull together the newest information on cluster analysis--providing the reader with a pragmatic guide to its current uses, statistical techniques, validation methods, and compatible software programs. (booktopia.com.au)
  • In agglomerative methods of hierarchical cluster analysis , the clusters obtained at the previous step are fused into larger clusters. (statistics.com)
  • Agglomerative methods start with N clusters comprising a single object, then on each step two clusters from the previous step are fused into a bigger cluster. (statistics.com)
  • This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. (statistics.com)
  • Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables. (statistics.com)
  • In statistics, single-linkage clustering is one of several methods of hierarchical clustering. (wikipedia.org)
  • The function used to determine the distance between two clusters, known as the linkage function, is what differentiates the agglomerative clustering methods. (wikipedia.org)
  • how can I determine how many clusters are appropriate for a kmeans analysis of my data? (stackoverflow.com)
  • See http://www.statmethods.net/advstats/cluster.html & http://www.mattpeeples.net/kmeans.html for more. (stackoverflow.com)
  • An alternative to starting with an initial clustering is to start with an initialization of the centroids - for example, as the first K cases in the dataset or as K cases randomly chosen from it. (encyclopedia.com)
  • Clustering is defined as an unsupervised classification of a dataset. (oreilly.com)
  • The data cleaning process When working with a real dataset we need to take into account the fact that some data might be missing or corrupted, therefore we need to prepare the dataset for our analysis. (pearltrees.com)
  • Cluster analysis, or clustering, is a core unsupervised learning technique that attempts to group the data points in a dataset into groups, or clusters, such that data points in the same cluster are similar to one another while being sufficiently different from data points in another cluster. (zenoss.com)
  • The best-track 1950-2002 dataset is described by seven distinct clusters. (columbia.edu)
  • The objective of conducting a cluster analysis is to discover if members of the dataset can be classified as pertaining to one of a small number of types. (thefreelibrary.com)
  • In order to facilitate user understanding, we use the provided example dataset to illustrate the standard analysis work-flow of PASCCA. (github.com)
  • The major difference between a traditional dissimilarity/similarity measure and that the former uses only a single viewpoint which is the origin while the latter utilizes many different viewpoints which are objects assumed to not be in the same cluster with the two objects being measures. (techrepublic.com)
  • The objects in one cluster are more related and have high similarity when compared to the objects that are in other cluster. (bartleby.com)
  • Find clusters of DNA sequences based on their global similarity to two reference sequences. (wolfram.com)
  • If the sequences are of vastly different lengths, the clustering procedure will group them in different clusters, even if they share a region of high sequence similarity. (rcsb.org)
  • Clustering is the unsupervised classification of data points into groups or clusters, such that samples in the same group are similar to each other, while patterns in different groups are dissimilar. (hindawi.com)
  • The municipality of Eindhoven and CBS have jointly conducted a statistical cluster analysis on all Eindhoven residents aged 16 and over. (cbs.nl)
  • The report provides a comprehensive statistical description of the individual clusters and shows their mutual connection as well as an overall picture. (cbs.nl)
  • You'll also explore statistical analysis examining the relative effectiveness of soft, hard, and smart power strategies. (coursera.org)
  • It is a main task of exploratory data mining , and a common technique for statistical data analysis , used in many fields, including machine learning , pattern recognition , image analysis , information retrieval , bioinformatics , data compression , and computer graphics . (wikipedia.org)
  • Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions . (wikipedia.org)
  • The LOVE clustering approach is a rigorous, adaptable, and scalable latent model-based statistical method that can be used in basic science or medical research to identify potentially significant biological or functional pathways. (pcrm.org)
  • Cluster analysis is a statistical method used to group similar objects into respective categories. (surveygizmo.com)
  • We outline a general methodology for model-based clustering that provides a principled statistical approach to these issues. (psu.edu)
  • When the clusters are relatively homogeneous (that is, the intra-cluster correlation is small), parallel studies tend to deliver better statistical performance than a stepped wedge trial. (bmj.com)
  • To identify differences between states, we implemented hierarchical cluster analysis (8-10) using the hclust function in R version 3.2.5 (free statistical computing software) with Euclidean distance as the distance measure and included the adjusted unhealthy behaviors and the prevention and outcome prevalence measures. (cdc.gov)
  • I sometimes find K-means clustering tough to explain as a statistical technique, but this makes for a great example: if you're a fielder facing Ichiro, it might be a good idea to keep an eye on those six spots when he hits. (smartdatacollective.com)
  • This paper presents a procedure for clustering analysis that combines Kohone's Self organizing Feature Map (SOFM) and statistical schemes. (repec.org)
  • The procedure outperformed others clustering techniques in the job of identifying consistent groups of countries from the economic and statistical viewpoints. (repec.org)
  • Although clustering--the classifying of objects into meaningful sets--is an important procedure, cluster analysis as a multivariate statistical procedure is poorly understood. (booktopia.com.au)
  • Agglomerative clustering starts with each case as a unique cluster, and with each step combines cases to form larger clusters until there is only one or a few larger clusters. (encyclopedia.com)
  • The clusters are then sequentially combined into larger clusters, until all elements end up being in the same cluster. (wikipedia.org)
  • Smyth, P., K. Ide, and M. Ghil, Multiple Regimes in Nothern Hemisphere Height Fields Via Mixture Model Clustering. (springer.com)
  • Growth in both the theory and applications of this clustering methodology has been steady since its inception. (scholarpedia.org)
  • The research methodology used to estimate and forecast the instrument cluster market begins with capturing data on key vendor revenues through secondary research. (marketsandmarkets.com)
  • Based on this novel methodology, we argue that verb cluster ordering in Dutch dialects can be reduced to three grammatical parameters (largely similar to the ones described in Barbiers et al. (jhu.edu)
  • It develops a methodology for clustering a large number of developing countries, identifying and ranking their welfare regimes, assessing their stability over the decade 1990-2000, and relating these to important structural variables. (bath.ac.uk)
  • In this paper , Italian school buildings' stock was analyzed by cluster analysis with the aim of providing a methodology able to identify the best energy retrofit interventions from the perspective of cost-benefit , and to correlate them with the specific characteristics of the educational buildings. (buildup.eu)
  • Hierarchical clustering follows one of two approaches. (encyclopedia.com)
  • While there are no best solutions for the problem of determining the number of clusters to extract, several approaches are given below. (pearltrees.com)
  • Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. (nih.gov)
  • These are the two basic approaches used in cluster analysis. (outsource2india.com)
  • Apply various approaches to clustering, including K-Means and Kohonen/SOM networks. (sas.com)
  • Cluscorr98 for Excel 2007: Clustering, multivariate visualization, and validation. (springer.com)
  • This document demonstrates, on several famous data sets, how the dendextend R package can be used to enhance Hierarchical Cluster Analysis (through better visualization and sensitivity analysis). (r-project.org)
  • Appro ( http://www.appro.com ), a leading provider of supercomputing solutions, today announces the deployment of Appro HyperPower™ Clusters, based on the Appro CPU/GPU GreenBlade System to provide Lawrence Livermore National Laboratory (LLNL) Computing Center with a new visualization cluster called "Edge" geared to support data analysis and visualization projects. (prweb.com)
  • Post-processing tasks are heavily I/O bound, so specialized visualization servers that optimize I/O rather than CPU speed are better suited for this work, which will be now enabled through the "Edge" cluster. (prweb.com)
  • The inclusion of GPU boards provides a critical technology for the increasingly complicated visualization and data analysis applications needed to support petascale simulations and beyond," said Bert Still, Exascale Computing Research Project Leader for LLNL. (prweb.com)
  • Appro is proud to be able to provide Lawrence Livermore National Laboratory with a powerful GPU cluster for its visualization and exascale software development computing projects," said John Lee, VP of Advanced Technology Solutions for Appro. (prweb.com)
  • This cluster solution demonstrates Appro's continued growth for hybrid computing deployments requiring higher memory for I/O bandwidth needed for efficient data analysis and complex visualization tasks. (prweb.com)
  • available complete software platform for comprehensive and integrated analysis and visualization of large proteomics datasets. (bestsoftware4download.com)
  • Clustering allows researchers to identify and define patterns between data elements. (surveygizmo.com)
  • We can distinguish and sparse regions in object space by automated clustering and from that we can find general interesting correlations and overall distribution patterns among data attributes. (bartleby.com)
  • In business, clustering can offer marketers some assistance with discovering distinct groups in their client bases and portray client groups taking into account the purchasing patterns. (bartleby.com)
  • Liu SH, Li Y, Liu B. Exploratory Cluster Analysis to Identify Patterns of Chronic Kidney Disease in the 500 Cities Project. (cdc.gov)
  • We used cluster analysis to explore patterns of chronic kidney disease in 500 of the largest US cities. (cdc.gov)
  • To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel-time series. (dtu.dk)
  • Empirically derived eating patterns using factor or cluster analysis: a review. (nih.gov)
  • This paper reviews studies performed to date that have employed cluster or factor analysis to empirically derive eating patterns. (nih.gov)
  • Since 1980, at least 93 studies were published that used cluster or factor analysis to define dietary exposures, of which 65 were used to test hypotheses or examine associations between patterns and disease outcomes or biomarkers. (nih.gov)
  • Studies were conducted in diverse populations across many countries and continents and suggest that patterns are associated with many different biomarkers and disease outcomes, whether measured by cluster or factor analysis. (nih.gov)
  • Hierarchical cluster analysis indicated distinct patterns of vestibular end-organ impairment, showing that the results for the same end-organs on both sides are more similar than to other end-organs. (frontiersin.org)
  • Hierarchical cluster analysis may help differentiate characteristic patterns of BVL. (frontiersin.org)
  • The cluster analysis is conducted with the aim of assigning data points (sequences) into reasonably homogenous groups (clusters). (thefreelibrary.com)
  • This study aimed to develop a novel, practical sequencing protocol that covered both conserved and variable regions of the viral genome and assess the influence of each subregion, sequence concatenation and unrelated reference sequences on phylogenetic clustering analysis. (plos.org)
  • NS5B concatenation, the inclusion of reference sequences and removal of HVR1 all influenced clustering outcome. (plos.org)
  • Seven HCV genotypes (1 to 7) with approximately 100 sub-types (1a, 1b, etc.) have been identified on the basis of molecular phylogenetic analyses of HCV sequences [ 4 ]. (plos.org)
  • D2_cluster: A Validated Method for Clustering EST and Full-length cDNA Sequences' John Burke, Dan Davison, and Winston Hide. (bio.net)
  • BACKGROUND: A computational system for analysis of the repetitive structure of genomic sequences is described. (jcvi.org)
  • The associated software (RepeatFinder), should prove helpful in the analysis of repeat structure for both complete and partial genome sequences. (jcvi.org)
  • Joe Ward ' s method (1963) is based on the sum of squares between the two clusters, summed over all variables. (encyclopedia.com)
  • The centroid method is based on the distance between cluster centroids. (encyclopedia.com)
  • If the within-cluster type of distribution is specified (such as multivariate normal), then the method of maximum likelihood can be used to estimate the parameters. (encyclopedia.com)
  • In this paper, a method for detecting the optimal cluster number is proposed. (hindawi.com)
  • The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. (hindawi.com)
  • This is the most common method of clustering. (surveygizmo.com)
  • This method is used to quickly cluster large datasets. (surveygizmo.com)
  • Which clustering method should be used? (psu.edu)
  • scale(mydata) # standardize variables Partitioning K-means clustering is the most popular partitioning method. (pearltrees.com)
  • The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. (dtu.dk)
  • The rows are ordered based on the order of the hierarchical clustering (using the "complete" method). (r-project.org)
  • In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. (nih.gov)
  • In the first method, objects/respondents are segmented into a pre-decided number of clusters. (outsource2india.com)
  • In this case, a method called non-hierarchical or k-means clustering can be used, which will partition the data into the specified number of clusters. (outsource2india.com)
  • Healthy beagle dogs under fasting conditions were used for in vivo studies and plasma samples were analyzed by a fluorescence polarization immunoassay analysis (FPIA method). (biomedsearch.com)
  • We adopted the widely-used clustering method, hierarchical clustering, to cluster genes, which was implemented by the R function using "hclust" default parameters. (github.com)
  • heatmap.2, as default uses euclidean measure to obtain distance matrix and complete agglomeration method for clustering, while heatplot uses correlation , and average agglomeration method, respectively. (stackoverflow.com)
  • The Core-E2 region, which represented the highest genetic diversity and longest sequence length in this study, provides an ideal method for clustering analysis to address a range of molecular epidemiological questions. (plos.org)
  • CONCLUSIONS: We propose a new clustering method for analysis of the repeat data captured in suffix trees. (jcvi.org)
  • The agglomerative method uses a bottom-up approach, i.e., starts with the individual objects, each considered to be in its own cluster, and then merges the clusters until the desired number of clusters is achieved. (igi-global.com)
  • Analysts use this method implicitly when viewing data graphically to identify clusters or other structure in data visually. (salford-systems.com)
  • In the first analytical step (based on the single-linkage method) companies are shown to mostly gather into a main cluster, although with the presence of several isolated outlier companies. (francoangeli.it)
  • The second step (based on Ward's method) proposes a more detailed analysis resulting in 4 clusters during the specified period. (francoangeli.it)
  • A drawback of this method is that it tends to produce long thin clusters in which nearby elements of the same cluster have small distances, but elements at opposite ends of a cluster may be much farther from each other than two elements of other clusters. (wikipedia.org)
  • The method is also known as nearest neighbour clustering. (wikipedia.org)
  • It is sometimes suggested that researchers start with hierarchical clustering to generate initial centroids, and then use nonhierarchical clustering. (encyclopedia.com)
  • for example, hierarchical clustering builds models based on distance connectivity. (wikipedia.org)
  • Automatic validation of hierarchical clustering. (springer.com)
  • On validation of hierarchical clustering. (springer.com)
  • Hierarchical clustering can group variables together in a manner similar to factor analysis . (surveygizmo.com)
  • From data to distances and then finally to results of (hierarchical) clustering. (wias-berlin.de)
  • So, hierarchical clustering of millions of observations is possible. (wias-berlin.de)
  • Typical cluster models include: Connectivity models: for example, hierarchical clustering builds models based on distance connectivity. (wikipedia.org)
  • SAS code for hierarchical clustering. (sas.com)
  • In this study, LOVE generates meaningful clusters from datasets spanning from a large range of biological areas and is used to identify and accurately quantify similarities and differences in the data. (pcrm.org)
  • Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. (researchandmarkets.com)
  • This can be done by forming all pairs of objects, with one object in one cluster and one in the other, and computing the distances between the members of these pairs. (encyclopedia.com)
  • Without loss of generality, we focus on visualisation of clustering based on pairwise distances. (springer.com)
  • The shortest of these pairwise distances that remain at any step causes the two clusters whose elements are involved to be merged. (wikipedia.org)
  • In supervised clustering, vectors are classified with respect to known reference vectors. (pnas.org)
  • In unsupervised clustering, no predefined reference vectors are used. (pnas.org)
  • The problem of clustering multivariate observations is viewed as the replacement of a set of vectors with a set of labels and representative vectors. (bell-labs.com)
  • Fuzzy c-means (FCM) clustering processes \(n\) vectors in \(p\)-space as data input, and uses them, in conjunction with first order necessary conditions for minimizing the FCM objective functional, to obtain estimates for two sets of unknowns. (scholarpedia.org)
  • The other set of unknowns in the original FCM model is a set of \(c\) cluster centers or prototypes, arrayed as the \(c\) columns of a \(p\times c\) matrix \(V\ .\) These prototypes are vectors (points) in the input space of \(p\)-tuples. (scholarpedia.org)
  • Searching for groupings, or clusters , is an important exploratory technique. (encyclopedia.com)
  • Cluster analysis is an exploratory tool designed to reveal natural groupings within a large group of observations, segmenting the survey sample - respondents or companies - into a small number of groups. (b2binternational.com)
  • Cluster analysis can suggest, based on complex input, groupings that would not otherwise be apparent, such as the needs of specific groupings or segments in the market. (b2binternational.com)
  • We cannot promise that we can find clusters or groupings in data that you will find useful. (salford-systems.com)
  • We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. (pnas.org)
  • Typically, it's good to conduct some data filtering prior to the analysis: This could include removing spots that are outside of the tissue or removing spots or genes that have a low number of reads. (bioconductor.org)
  • The package PASCCA is an easy-to-use R package for analyses of APA related gene expression, including the characterization of poly(A) sites, quantification of association between genes with/without repeated measurements, clustering of APA-related genes to infer significant APA specific gene modules, and the evaluation of clustering performance with a variety of indexes. (github.com)
  • The resulting cluster suggests some functional relationships between genes, and some known genes belongs to a unique functional classes shall provide indication for unknown genes in the same clusters. (spie.org)
  • We describe a core gene cluster, comprised of eight genes (designated CTB1-8 ), and associated with cercosporin toxin production in Cercospora nicotianae . (wiley.com)
  • Sequence analysis identified 10 putative open reading frames (ORFs) flanking the previously characterized CTB1 and CTB3 genes that encode, respectively, the polyketide synthase and a dual methyltransferase/monooxygenase required for cercosporin production. (wiley.com)
  • Disruption of the CTB2 gene encoding a methyltransferase or the CTB8 gene yielded mutants that were completely defective in cercosporin production and inhibitory expression of the other CTB cluster genes. (wiley.com)
  • A criterion such as between-groups sum of squares or likelihood can be plotted against the number of clusters in a scree plot . (encyclopedia.com)
  • Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster ) are more similar (in some sense) to each other than to those in other groups (clusters). (wikipedia.org)
  • To be precise, in the first stage I need to create clusters on the basis of a set of variables, s1, and in the second stage I need to create clusters, within the groups formed in the first stage, using a different set of variables, s2. (stata.com)
  • The goal of performing a cluster analysis is to sort different objects or data points into groups in a manner that the degree of association between two objects is high if they belong to the same group, and low if they belong to different groups. (surveygizmo.com)
  • For example, when cluster analysis is performed as part of market research , specific groups can be identified within a population. (surveygizmo.com)
  • The analysis of these groups can then determine how likely a population cluster is to purchase products or services. (surveygizmo.com)
  • If these groups are defined clearly, a marketing team can then target varying cluster with tailored, targeted communication. (surveygizmo.com)
  • Clustering or Cluster analysis is defined as the process of organizing objects into groups whose members are similar in some way. (bartleby.com)
  • Clustering can be utilized to organize the query results in groups and present the outcomes in a concise and effectively available way. (bartleby.com)
  • Cluster analysis is the automated search for groups of related observations in a data set. (psu.edu)
  • The analyst groups objects so that objects in the same group (called a cluster) are more similar to each other than to objects in other groups (clusters) in some way. (wikipedia.org)
  • Cluster Randomised Controlled Trials (cRCTs) are trials which randomise groups of patients rather than individual patients. (sheffield.ac.uk)
  • Finding Groups in Data: An Introduction to ClusterAnalysis . (program-transformation.org)
  • Value cluster analysis is a research technique that is used for classifying objects into groups. (outsource2india.com)
  • The result of value cluster analysis is a grouping of the data into groups called clusters. (outsource2india.com)
  • Clustering is a form of unsupervised machine learning, where instances are organized into groups whose members share similarities. (frontiersin.org)
  • A more significant clustering is one which groups distinct combinations into separate clusters. (bookdepository.com)
  • Kaleidoscope Pro detects similar vocalizations and quickly sorts them into groups to streamline your analysis. (wildlifeacoustics.com)
  • Kaleidoscope Pro automatically scans your recordings and pulls out distinct sounds and phrases, such as frog calls or bird songs, and groups them into clusters. (wildlifeacoustics.com)
  • Cluster analysis is a systematic quantitative technique used to discover groups in data (Kaufman & Rousseeuw 1990). (yogamag.net)
  • Firstly, segmentations lie at the core of many submissions to IJMR, but simply because a cluster analysis has produced a number of discrete groups of consumers, that does not mean it provides a valid interpretation of the market. (mrs.org.uk)
  • Secondly, segmentations need to facilitate action, but often the clusters are somewhat meaningless if it is not possible to use the output in a practical way, for example, to develop a marketing strategy that can communicate differentiated messages to the target groups. (mrs.org.uk)
  • We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. (psu.edu)
  • Windows and/or Unix-like systems to a homogeneous, load-releasing cluster . (bestsoftware4download.com)
  • The refurbished buildings were classified in homogeneous clusters and, for each of them, the most representative building was identified. (buildup.eu)
  • consumers are clustered according to psychographic, demographic, and purchasing behavior variables. (encyclopedia.com)
  • Through close collaboration and an iterative process, a division was made into nine clusters of residentsbased on 25 demographic and socioeconomic characteristics. (cbs.nl)
  • Cluster analysis of 500 US cities, summarized at the state level, plus Washington, DC, based on kidney disease-related factors (unhealthy behaviors, prevention measures, and outcomes related to CKD) and adjusted for socio-demographic characteristics. (cdc.gov)
  • We used the regression residuals for each measure (hereafter "adjusted prevalence measures") in the subsequent cluster analysis, as they indicate the portion of variability that cannot be explained by the socio-demographic characteristics. (cdc.gov)
  • An example of its use can be clustering similar locations across the USA based on various demographic characteristics like average income, number of Housing Starts (an economic indicator that tracks the number of new single-family homes or buildings that were constructed throughout the month), the number of people in an age group or income group, the number of sports goods shops, etc. (outsource2india.com)
  • Demographic clusters. (sas.com)
  • Detection of BotNets starts with monitoring the Internet traffic, followed by analysis and clustering of the data to compare it with the neighboring nodes to determine a bot-infection (Fig. 2). (computerweekly.com)
  • for example, DBSCAN and OPTICS defines clusters as connected dense regions in the data space. (wikipedia.org)
  • Here, researchers define the number of clusters prior to performing the actual study. (surveygizmo.com)
  • Ideally, the mean values of the variables used to form the clusters should also be used to define them. (outsource2india.com)
  • Morales-Merino C. (2013) Visualisation of Cluster Analysis Results. (springer.com)
  • In Robust Cluster Analysis and Variable Selection , Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. (routledge.com)
  • The results from cluster analysis show practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. (dtu.dk)
  • as.numeric(which.max(fit$results[2,])) cat("Calinski criterion optimal number of clusters:", calinski.best, "\n") # 5 clusters! (stackoverflow.com)
  • Researchers can then analyze the clusters for their characteristics and give the clusters appropriate names based on the results. (outsource2india.com)
  • How are the Results of Cluster Analysis Interpreted? (outsource2india.com)
  • Kaleidoscope Pro presents the cluster or classifier results, along with other metadata such as timestamps, temperature, etc., in a table that is easily exported into Excel and other applications for pivot table and chart creation. (wildlifeacoustics.com)
  • The weaker "clusterability axiom" (no cycle has exactly one negative edge) yields results with more than two clusters, or subgraphs with only positive edges. (wikipedia.org)
  • The appropriate results depend on the analysis but I'm trying to understand why the defaults are so different, and how to get both functions to give the same result (or highly similar result) so that I understand all the 'blackbox' parameters that go into this. (stackoverflow.com)
  • Pairwise distance, genetic distance and bootstrap support were computed to assess the impact of HCV region on clustering results as measured by the identification and percentage of participants falling within all clusters, cluster size, average patristic distance, and bootstrap value. (plos.org)
  • Our results demonstrated that the genomic region of HCV analysed influenced phylogenetic tree topology and clustering results. (plos.org)
  • Projection or mapping of analysis results into a biological context. (scribd.com)
  • We provide a quantitative analysis of word-order variation in verb clusters in 185 dialects of Dutch and map the results of that analysis against linguistic parameters extracted from the theoretical literature on verb clusters. (jhu.edu)
  • Dendrograms of the cluster analysis results are drawn automatically. (bestsoftware4download.com)
  • Clustering results are inconsistent. (sas.com)
  • Furthermore, we use these results to contrast clustering with concept analysis techniques. (program-transformation.org)
  • A general criterion for clustering is derived as a measure of representation error. (bell-labs.com)
  • Calinsky criterion: Another approach to diagnosing how many clusters suit the data. (stackoverflow.com)
  • However, if substantial cluster-level effects are present (that is, larger intra-cluster correlations) or the clusters are large, the stepped wedge design will be more powerful than a parallel design, even one in which the intervention is preceded by a period of baseline control observations. (bmj.com)
  • What is needed instead is a holistic approach to analysis of genomic data that focuses on illuminating order in the entire set of observations, allowing biologists to develop an integrated understanding of the process being studied. (pnas.org)
  • Here n k denotes the number of observations of the k th cluster. (wias-berlin.de)
  • Data collection continues throughout the study, so that each cluster contributes observations under both control and intervention observation periods. (bmj.com)
  • We compare the resulting weak lensing mass profile and total mass estimate to those obtained from our re-analysis of XMM-Newton observations, derived under the hypothesis of hydrostatic equilibrium. (uio.no)
  • The mean profile of a cluster is the centroid , the set of means of the variables, for the individuals in that cluster. (encyclopedia.com)
  • The process begins by partitioning the cases into K initial clusters and assigning each case to the cluster whose centroid is nearest. (encyclopedia.com)
  • A new probabilistic clustering technique, based on a regression mixture model, is used to describe tropical cyclone trajectories in the western North Pacific. (columbia.edu)
  • In the case of Image Recognition the concept of clustering can be applied to identify the clusters in handwritten character recognition systems. (bartleby.com)
  • Model-based Gaussian clustering allows to identify clusters of quite different shapes, see the application to ecology in Figure 2. (wias-berlin.de)
  • This analysis technique is typically performed during the exploratory phase of research, since unlike techniques such as factor analysis , it doesn't make any distinction between dependent and independent variables. (surveygizmo.com)
  • Several techniques exist for analysing the data from such studies, but the essence of them is that the experimental unit (district or general practitioner) is the unit of analysis. (bmj.com)
  • Clustering techniques based on cores (representative points) are appropriate tools for data mining of large data sets. (wias-berlin.de)
  • Simulation studies were carried out in order to compare core-based clustering techniques with well-known model-based ones. (wias-berlin.de)
  • This is because the clustering techniques use a distance measure to find the closest objects to group into a cluster. (outsource2india.com)
  • We acquire data of terrorist events from reliable online sources, and apply data pre-processing techniques followed by cluster analysis. (bookdepository.com)
  • Web mining techniques such as clustering help to organize the web content into appropriate subject based categories so that their efficient search and retrieval becomes manageable. (techrepublic.com)
  • Advanced techniques for metabolomic data analysis. (scribd.com)
  • differences in heatmap/clustering defaults in R (heatplot versus heatmap.2)? (stackoverflow.com)
  • The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. (pnas.org)
  • The main task in the cluster analysis is to determine how many clusters are to be used (Cattrell, 1998). (thefreelibrary.com)
  • Determine the number of clusters present in data. (sas.com)
  • Determine which variables should be used in clustering. (sas.com)
  • Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. (hindawi.com)
  • in Workshop on Clustering High Dimensional Data and its Applications, SIAM Data Mining. (springer.com)
  • Data mining in telecommunications: case study of cluster analysis. (thefreelibrary.com)
  • Clustering analysis has been widely applied in diverse fields such as data mining, access structures, knowledge discovery, software engineering, organization of information systems, and machine learning. (igi-global.com)
  • CART and other Salford data mining modules now include an approach to cluster analysis, density estimation and unsupervised learning using ideas that we trace to Leo Breiman , but which may have been known informally in among statisticians at Stanford and elsewhere for some time. (salford-systems.com)
  • SAS Enterprise Miner - how many clusters? (sas.com)
  • Incorporating hierarchical clusters into SAS Enterprise Miner. (sas.com)
  • Application of model-based Gaussian clustering to ecology. (wias-berlin.de)
  • In the following, as a special approach in big data clustering, let us propose simple Gaussian core-based clustering. (wias-berlin.de)
  • The simplest Gaussian model is when the covariance matrix of each cluster is constrained to be diagonal. (wias-berlin.de)
  • There are 250 artificially generated Gaussian samples of size 250 with equal class probabilities drawn ( K = 2 clusters). (wias-berlin.de)
  • Therefore, a cluster is the collection of objects which are similar to each other and are dissimilar to the objects belonging to other clusters. (bartleby.com)
  • Ideally, the cases in each group should have a very similar profile towards specific characteristics - for example attitudinal or behavioral questions - while the profiles of respondents belonging to different clusters should be very dissimilar. (b2binternational.com)
  • We can visualize the result of running it by turning the object to a dendrogram and making several adjustments to the object, such as: changing the labels, coloring the labels based on the real species category, and coloring the branches based on cutting the tree into three clusters. (r-project.org)
  • The result of the clustering can be visualized as a dendrogram, which shows the sequence in which clusters were merged and the distance at which each merge took place. (wikipedia.org)
  • The bottom-up approach also included analyzing the different types of instrument clusters installed in various types of vehicles such as two-wheelers, passenger cars, agriculture vehicles, commercial vehicles, and off-highway vehicles, among others, considering the shipments of vehicles. (marketsandmarkets.com)
  • It was followed by multiplying the average selling price (ASP) of instrument clusters by the number of units to calculate the market size, in terms of value. (marketsandmarkets.com)
  • The major drivers for this market are rising demand for electric vehicles, growing demand for advanced cluster technology by OEMs, increasing demand for luxury vehicles, and decreasing price of digital instrument clusters. (yahoo.com)
  • So what we find is that there are distinct clusters in our data. (coursera.org)
  • Indeed, the command cluster creates three variables (stage1cl_id, stage1cl_ord and stage1cl_hgt). (stata.com)
  • Data Preparation Prior to clustering data, you may want to remove or estimate missing data and rescale variables for comparability. (pearltrees.com)
  • After this, the clusters will be analyzed based on the mean values of the variables used for clustering, as in the first step. (outsource2india.com)
  • Incorporate categorical and binary variables in clustering. (sas.com)
  • If the terminal nodes are sufficiently pure in a single target class the analysis will be considered successful even if two or more terminal nodes are very similar on most predictor variables. (salford-systems.com)
  • More in detail, after finding that the trend of the means of six financial ratios has improved in the defined period, with the exception of ROE, the six ratios are used as variables to perform a cluster analysis. (francoangeli.it)
  • They give a nearly global-optimal discrete clustering solution by using singular value decomposition and nonmaximum suppression in an i. (psu.edu)
  • See http://www.jstatsoft.org/v18/i06/paper # http://www.stat.washington.edu/research/reports/2006/tr504.pdf # library(mclust) # Run the function to see how many clusters # it finds to be optimal, set it to search for # at least 1 model and up 20. (stackoverflow.com)
  • This choice may not be optimal, as it should be made in the very beginning, when there may not exist an informal expectation of what the number of natural clusters would be. (igi-global.com)
  • Many applications of clustering are also found in Web search. (bartleby.com)
  • Websites providing cluster headache information were determined on the search engine MetaCrawler and classified as either patient oriented or healthcare provider oriented. (ingentaconnect.com)
  • Customize cluster settings to help you more easily search for a specific species or refine for classifiers. (wildlifeacoustics.com)
  • A well-known clustering is that of stars into a main sequence, white giants, and red dwarfs, according to temperature and luminosity. (encyclopedia.com)
  • and 'A comprehensive approach to clustering of expressed human gene sequence: The Sequence Tag Alignment and Consensus Knowledgebase. (bio.net)
  • To reduce the number of hits, a 40% sequence identity clustering has been applied and a representative chain taken from each cluster. (rcsb.org)
  • The clusterings are assigned sequence numbers 0 , 1 , … , n − 1 {\displaystyle 0,1,\ldots ,n-1} and L ( k ) {\displaystyle L(k)} is the level of the k {\displaystyle k} -th clustering. (wikipedia.org)
  • Gasch, A.P. and M.B. Eisen, Exploring the Conditional Coregulation of Yeast Gene Expression through Fuzzy K-Means Clustering. (springer.com)
  • Although the responsible gene cluster has been identified, the biosynthetic pathway remains to be elucidated. (mdpi.com)
  • In the present study, members of the gene cluster were deleted individually in a Fusarium graminearum strain overexpressing the local transcription factor. (mdpi.com)
  • By providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data. (github.com)
  • STACK_PACK 1.0 has been developed by SANBI in collaboration with Electric Genetics, Cape Town (PTY) LTD, to support analysis of the increasing EST load for Gene Discovery. (bio.net)
  • The result of a cluster analysis shown as the coloring of the squares into three clusters. (wikipedia.org)
  • Used to describe and quantify customer segments, cluster analysis enables you to target customers according to their needs, instead of having one general marketing approach. (b2binternational.com)
  • Google research scientist Peter Hauck used Weka and k-means cluster analysis to describe where Mariners right-fielder Ichiro favours hitting the baseball. (smartdatacollective.com)
  • Once grouped, view, sort and label each cluster to efficiently analyze your recordings. (wildlifeacoustics.com)
  • Clay sediments analysis in the troad and its segmentation. (springer.com)
  • Contour and texture analysis for image segmentation - Malik, Belongie, et al. (psu.edu)
  • It can also be referred to as segmentation analysis, taxonomy analysis, or clustering. (surveygizmo.com)
  • The idea is to cluster the data in two stages: run SOFM and then minimize the segmentation dispersion. (repec.org)
  • The marketing application of value cluster analysis is in customer segmentation and in the estimation of segment sizes. (outsource2india.com)
  • The aim of this paper is to present a case study on the segmentation of the industrial market in a telecommunication company by means of cluster analysis. (thefreelibrary.com)
  • Use clustering and segmentation. (sas.com)
  • Segmentation analysis: Market by display type, by display size, by embedded technology, by end use, and by region. (yahoo.com)
  • n = 91 clusters with feeding events) to estimate caracal diet in South Africa's Succulent Karoo, a global biodiversity hotspot. (bioone.org)
  • Market size estimate: Digital instrument cluster market size estimation in terms of value ($M) shipment. (yahoo.com)
  • We present a mass estimate of the Planck-discovered cluster PLCK G100.2-30.4, derived from a weak lensing analysis of deep Subaru griz images. (uio.no)