The formation of a partition of objects, each with an associated random measurement X, is given operational meaning and a figure of merit. The information about X given each object is first reduced to information about X given the cluster in the partition to which that object belongs. The figure of merit for a partition is then the probability of a correct object identification, on the basis of a realization of X, after the information loss. This leads both to a method for evaluating partitions and a clustering algorithm. 23 pp. Ref.
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