Application of the cross-entropy method to clustering and vector quantization

Kroese, Dirk P., Rubinstein, Reuven Y. and Taimre, Thomas (2007) Application of the cross-entropy method to clustering and vector quantization. Journal of Global Optimization, 37 1: 137-157. doi:10.1007/s10898-006-9041-0

Author Kroese, Dirk P.
Rubinstein, Reuven Y.
Taimre, Thomas
Title Application of the cross-entropy method to clustering and vector quantization
Journal name Journal of Global Optimization   Check publisher's open access policy
ISSN 0925-5001
Publication date 2007
Year available 2006
Sub-type Article (original research)
DOI 10.1007/s10898-006-9041-0
Volume 37
Issue 1
Start page 137
End page 157
Total pages 21
Editor P. M. Pardalos
Place of publication Netherlands
Publisher Springer
Collection year 2008
Language eng
Subject 230202 Stochastic Analysis and Modelling
230118 Optimisation
780101 Mathematical sciences
Abstract We apply the cross-entropy (CE) method to problems in clustering and vector quantization. The CE algorithm for clustering involves the following iterative steps: (a) generate random clusters according to a specified parametric probability distribution, (b) update the parameters of this distribution according to the Kullback-Leibler cross-entropy. Through various numerical experiments, we demonstrate the high accuracy of the CE algorithm and show that it can generate near-optimal clusters for fairly large data sets. We compare the CE method with well-known clustering and vector quantization methods such as K-means, fuzzy K-means and linear vector quantization, and apply each method to benchmark and image analysis data.
Q-Index Code C1
Q-Index Status Confirmed Code
Additional Notes Published online 10 August 2006 and in journal 2007.

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Created: Fri, 28 Mar 2008, 11:17:31 EST by Marie Grove on behalf of School of Mathematics & Physics