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Biometrika 1981 68(1):275-285; doi:10.1093/biomet/68.1.275
© 1981 by Biometrika Trust
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Approximations to Bayesian clustering rules

DAVID A. BINDER

Faculty of Administration, University of Ottawa Canada

The amount of computation required for implementing the Bayesian cluster analysis suggested by Binder (1978) is often too large for exact results to be feasible. A general algorithm is proposed for approximating the similarity matrix and the resulting optimal partition. This algorithm is applied to artificial and to real data. For the real data, it appears that the algorithm is successful at identifying the optimal partitions as well as those units whose group membership is doubtful.

Key Words: Bayes rule • Cluster analysis • Multiple comparisons


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