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Soft Computing

, Volume 1, Issue 4, pp 166–179 | Cite as

A geometric approach to cluster validity for normal mixtures

  • J. C. Bezdek
  • W. Q. Li
  • Y. Attikiouzel
  • M. Windham
Original paper

Abstract

 We study indices for choosing the correct number of components in a mixture of normal distributions. Previous studies have been confined to indices based wholly on probabilistic models. Viewing mixture decomposition as probabilistic clustering (where the emphasis is on partitioning for geometric substructure) as opposed to parametric estimation enables us to introduce both fuzzy and crisp measures of cluster validity for this problem. We presume the underlying samples to be unlabeled, and use the expectation-maximization (EM) algorithm to find clusters in the data. We test 16 probabilistic, 3 fuzzy and 4 crisp indices on 12 data sets that are samples from bivariate normal mixtures having either 3 or 6 components. Over three run averages based on different initializations of EM, 10 of the 23 indices tested for choosing the right number of mixture components were correct in at least 9 of the 12 trials. Among these were the fuzzy index of Xie-Beni, the crisp Davies-Bouldin index, and two crisp indices that are recent generalizations of Dunn’s index.

Keywords cluster validity EM algorithm generalized Dunn’s index mixture decomposition normal mixtures Xie-Beni index 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • J. C. Bezdek
    • 1
  • W. Q. Li
    • 2
  • Y. Attikiouzel
    • 2
  • M. Windham
    • 3
  1. 1.Department of Computer Science, University of West Florida Pensacola, FL 32514 USAUS
  2. 2.Department of Electrical and Electronic Engineering, University of Western Australia Nedlands, Perth Western Australia, 6009, AustraliaAU
  3. 3.Department of Mathematics and Statistics, University of South Alabama Mobile, AL 36688 USAUS

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