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Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw
Publisher: Wiley-Interscience

The techniques of global partitioning of the data, such as K-means, partitioning around medoids, various flavors of hierarchical clustering, and self-organized maps [1-4], have provided the initial picture of similarity in the gene expression profiles, Another approach to finding functionally relevant groups of genes is network derivation, which has been popular in the analysis of gene-gene and protein-protein interactions [6-10], and is also applicable to gene expression analysis [11,12]. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. The data comes from a questionnaire. Clustering Large and High Dimensional data. Mirkin B: Mathematical Classification and Clustering. We assume an infinite set of latent groups, where each group is described by some set of parameters. Stephan Holtmeier, who is a psychologist by background, presented an introduction to cluster analysis with R, motivated by his work in analysing survey data. Finding Groups in Data: An Introduction to Cluster Analysis. Audience The following groups will find this book a valuable tool and reference: applied statisticians; engineers and scientists using data analysis; researchers in pattern recognition, artificial intelligence, machine learning, and data mining; and applied mathematicians. Instructors can also use it as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining. Let's describe a generative model for finding clusters in any set of data. The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. My research question is about elderly people and I have to find out underlying groups. Kogan J., Nicholas C., Teboulle M. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Clustering is a main task of explorative data mining, and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. €� John Wiley & Sons, 1990 Collective Intelligence.