ABSTRACT
In this chapter, we discuss the most general case of the relational data clustering, which makes use of all three types of information: heterogeneous relations altogether, homogeneous relations, and attributes, to cluster multiple-typerelated data objects simultaneously. 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.
