document: A probabilistic framework for relational clustering Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects , such as Web mining , search marketing , bioinformatics , citation analysis , and epidemiology . In this paper , we propose a probabilistic model for relational clustering , which also provides a principal framework to unify various important clustering tasks including traditional attributes-based clustering , semi-supervised clustering , co-clustering and graph clustering . The proposed model seeks to identify cluster structures for each type of data objects and interaction patterns between different types of objects . Under this model , we propose parametric hard and soft relational clustering algorithms under a large number of exponential family distributions . The algorithms are applicable to relational data of various structures and at the same time unifies a number of stat-of-the-art clustering algorithms : co-clustering algorithms , the k-partite graph clustering , Bregman k-means , and semi-supervised clustering based on hidden Markov random fields .
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