document: Model-based overlapping clustering While the vast majority of clustering algorithms are partitional , many real world datasets have inherently overlapping clusters . Several approaches to finding overlapping clusters have come from work on analysis of biological datasets . In this paper , we interpret an overlapping clustering model proposed by Segal et al. ( 23 ) as a generalization of Gaussian mixture models , and we extend it to an overlapping clustering model based on mixtures of any regular exponential family distribution and the corresponding Bregman divergence . We provide the necessary algorithm modifications for this extension , and present results on synthetic data as well as subsets of 20-Newsgroups and EachMovie datasets .
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