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

Finding Groups in Data: An Introduction to Cluster Analysis. Clustering Large and High Dimensional data. €� John Wiley & Sons, 1990 Collective Intelligence. Hierarchical Cluster Analysis Some Basics and Algorithms 1. Cluster analysis is called Q-analysis (finding distinct ethnic groups using data about believes and feelings1), numerical taxonomy (biology), classification analysis (sociology, business, psychology), typology2 and so on. Cluster analysis is a collection of statistical methods, which identifies groups of samples that behave similarly or show similar characteristics. €�Finding Groups in Data: An Introduction to Cluster Analysis” JohnWiley & Sons, New York. Kogan J., Nicholas C., Teboulle M. 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. Finding groups in data: An introduction to cluster analysis. Mirkin B: Mathematical Classification and Clustering. 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. Introduction 1.1 What is cluster analysis?