Bayesian model-based clustering procedures

Amer Statistical Assoc
Publication Type:
Journal Article
Journal of Computational and Graphical Statistics, 2006, 16 (3), pp. 526 - 558
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This paper establishes a general framework for Bayesian model-based clustering, in which subset labels are exchangeable, and items are also exchangeable, possibly up to covariate e®ects. It is rich enough to encompass a variety of existing procedures, including some recently discussed methodologies involving stochastic search or hierarchical clustering, but more importantly allows the formulation of clustering procedures that are optimal with respect to a speci¯ed loss function. Our focus is on loss functions based on pairwise coincidences, that is, whether pairs of items are clustered into the same subset or not.
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