Entropy-based criterion in categorical clustering

  • Authors:
  • Tao Li;Sheng Ma;Mitsunori Ogihara

  • Affiliations:
  • University of Rochester, Rochester, NY;IBM T. J. Watson Research Center, Hawthorne, NY;University of Rochester, Rochester, NY

  • Venue:
  • ICML '04 Proceedings of the twenty-first international conference on Machine learning
  • Year:
  • 2004

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Abstract

Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and establishes the connection between the criterion and the approach based on dissimilarity co-efficients. An iterative Monte-Carlo procedure is then presented to search for the partitions minimizing the criterion. Experiments are conducted to show the effectiveness of the proposed procedure.