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We introduce novel dissimilarity into a probabilistic clustering task to properly measure dissimilarity among multiple clusters when each cluster is characterized by a subpopulation in the mixture model. This measure of dissimilarity is called redundancy-based dissimilarity among probability distributions. From aspects of both source coding and a statistical hypothesis test, we shed light on several of the theoretical reasons for the redundancy-based dissimilarity among probability distributions being a reasonable measure of dissimilarity among clusters. We also elucidate a principle in common for the measures of redundancy-based dissimilarity and Ward’s method in terms of hierarchical clustering criteria. Moreover, we show several related theorems that are significant for clustering tasks. In the experiments, properties of the measure of redundancy-based dissimilarity are examined in comparison with several other measures.
clustering, mixture model, dissimilarity measure, information theory, Ward’s method
Akira Hayashi, Kazunori Iwata, "A Redundancy-Based Measure of Dissimilarity among Probability Distributions for Hierarchical Clustering Criteria", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 76-88, January 2008, doi:10.1109/TPAMI.2007.1160
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