Clustering with model-level constraints

In this paper we describe a systematic approach to uncovering multiple clusterings underlying a dataset. In contrast to previous approaches, the proposed method uses information about structures that are not desired and consequently is very useful in an exploratory datamining setting. Specifically, the problem is formulated as constrained model-based clustering where the constraints are placed at a model-level. Two variants of an EM algorithm, for this constrained model, are derived. The performance of both variants is compared against a state-of-the-art information bottleneck algorithm on both synthetic and real datasets. Copyright © by SIAM.