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Methodological study demonstrates Bayesian partitioning via a decision-theoretic similarity matrix, highlighting theoretical links to existing clustering techniques.
A parametric model for partitioning individuals into mutually exclusive groups is given. A Bayesian analysis is applied and a loss structure imposed. A model-dependent definition of a similarity matrix is proposed and estimates based on this matrix are justified in a decision-theoretic framework. Some existing cluster analysis techniques are derived as special limiting cases. The results of the procedure applied to two data sets are compared with other analyses.
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David A. Binder (1978) studied this question.
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