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Published in:
2006 | OriginalPaper | Chapter
Clustering with Entropy-Like k-Means Algorithms
Authors : M. Teboulle, P. Berkhin, I. Dhillon, Y. Guan, J. Kogan
Published in: Grouping Multidimensional Data
Publisher: Springer Berlin Heidelberg
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The aim of this chapter is to demonstrate that many results attributed to the classical
k
-means clustering algorithm with the squared Euclidean distance can be extended to many other distance-like functions. We focus on entropy-like distances based on Bregman [88] and Csiszar [119] divergences, which have previously been shown to be useful in various optimization and clustering contexts. Further, the chapter reviews various versions of the classical
k
-means and BIRCH clustering algorithms with squared Euclidean distance and considers modifications of these algorithms with the proposed families of distance-like functions. Numerical experiments with some of these modifications are reported.