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

The purpose of cluster analysis is to place objects into groups, or clusters, suggested by the data, not defined by a priori, such that objects in a given cluster tend to be similar to each other in some sense, and objects in different clusters tend to be dissimilar. 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. Mirkin B: Mathematical Classification and Clustering. United Kingdom The primary objective in both cases was to examine the class separability in order to get an estimate of classification complexity. Stephan Holtmeier, who is a psychologist by background, presented an introduction to cluster analysis with R, motivated by his work in analysing survey 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. Hoboken, New Jersey: Wiley; 2005. You can also use cluster analysis to summarize data rather than to find "natural" or "real" clusters; this use of clustering is sometimes called dissection. €�Finding Groups in Data: An Introduction to Cluster Analysis” JohnWiley & Sons, New York. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967, 1:281-297. Kaufman L, Rousseeuw PJ: Finding groups in data: an introduction to cluster analysis. Affect inference in learning environments: a functional view of facial affect analysis using naturalistic data.