Incremental Constrained Clustering: A Decision Theoretic Approach

  • Swapna Raj Prabakara Raj
  • Balaraman Ravindran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7867)

Abstract

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.

Keywords

Clustering Constraints Utility function Decision theory 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Swapna Raj Prabakara Raj
    • 1
  • Balaraman Ravindran
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

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