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.
10 Jobs - Browse the latest Cluster Analysis jobs on Guru. Apply online for freelance Cluster Analysis jobs. Be the first to send a quote and get hired.
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

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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. ...
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.
provide similar results. Options for hierarchical and agglomerative clustering are available for both of these functions. So, would you please help me to understand clearly the differences between them?. Just to be clear,my main question concerns about choosing appropriate function for different research questions and anlysis. That is, its not important if the two functions (or other similar functions) produce different outputs with the same meaning rather I want to know when to use which function and why. Also, please post any tutorial for cluster analysis in Mathematica if you aware of.. ...