Incremental constrained clustering: A decision theoretic approach

Typical constrained clustering algorithms incorporate a set of must-link and cannot-link constraints into the clustering process. These instance level constraints specify relationships between pairs of data items and are generally derived by a domain expert. Generating these constraints is considered as a cumbersome and expensive task. In this paper we describe an incremental constrained clustering framework to discover clusters using a decision theoretic approach. Our framework is novel since we provide an overall evaluation of the clustering in terms of quality in decision making and use this evaluation to "generate" instance level constraints. We do not assume any domain knowledge to start with. We show empirical validation of this approach on several test domains and show that we achieve better performance than a feature selection based approach. © Springer-Verlag 2013.