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The paper introduces a framework for clustering data objects in a similarity-based context. The aim is to cluster objects into a given number of classes without imposing a hard partition, but allowing for a soft assignment of objects to clusters. Our approach uses the assumption that similarities reflect the likelihood of the objects to be in a same class in order to derive a probabilistic model for...
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