Hennig, Christian; Meila, Marina; Murtagh, Fionn; Rocci,'s Handbook of cluster analysis PDF

By Hennig, Christian; Meila, Marina; Murtagh, Fionn; Rocci, Roberto

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A. R. Sokal. R. Anderberg, Cluster Analysis for Applications, Academic Press, New York, 1973. H. J. Hall, Isodata: A Method of Data Analysis and Pattern Classification, Stanford Research Institute, Menelo Park, 1965. P. Benzécri, L’Analyse des Données. I. ). K. S. Aldenderfer, The literature on cluster analysis, Multivariate Behavioral Research, 13, 271–295, 1978. -H. Bock, Automatische Klassifikation, Vandenhoek und Rupprecht, Göttingen, Germany, 1974. -H. Bock, Origins and extensions of the k-means algorithm in cluster analysis, Electronic Journal for History of Probability and Statistics, 4 (2), 26, 2008.

A. Gattone, and M. Vichi 2011. A new dimension reduction method: Factor discriminant k-means. Journal of Classification 28, 210–226. Rodriguez, A. and A. Laio 2014. Clustering by fast search and find of density peaks. Science 344, 1492–1496. Shepard, R. N. and P. Arabie 1979. Additive clustering representation of similarities as combinations of discrete overlapping properties. Psychological Review 86, 87–123. Slonim, N. and N. Friedman 2006. Multivariate information bottleneck. Neural Computation 18, 1739–1789.

Usually this is formalized as minimizing n d(xi , mc(i) ), where S(D, m1 , . . 1) i=1 c(i) = argmin d(xi , m j ), i = 1, . . ,K } by choice of the centroids m1 , . . , m K , where d is a dissimilarity measure. The centroids m1 , . . , m K may be required to be objects in D (in which case they are sometimes called “exemplars,”), or they may stem from the data space X . d may be the given dissimilarity measure that characterizes the objects or a transformation. For the K -means method, x1 , .

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