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)


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.


Clustering Constraints Utility function Decision theory 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means Clustering with Background Knowledge. In: 18th International Conference on Machine Learning (ICML), pp. 577–584. Morgan Kaufmann, San Francisco (2001)Google Scholar
  2. 2.
    Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: 17th International Conference on Machine Learning (ICML), pp. 1103–1110. Morgan Kaufmann, Stanford (2000)Google Scholar
  3. 3.
    Davidson, I., Ravi, S.S., Ester, M.: Efficient incremental constrained clustering. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 240–249. ACM, San Jose (2007)CrossRefGoogle Scholar
  4. 4.
    Basu, S., Banerjee, A., Mooney, R.J.: Active Semi-Supervision for Pairwise Constrained Clustering. In: SIAM International Conference on Data Mining (SDM). SIAM, Florida (2004)Google Scholar
  5. 5.
    Eaton, E.: Clustering with Propagated Constraints. University of Maryland Baltimore County (2005)Google Scholar
  6. 6.
    Miller, G.A.: The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review 63, 81–97 (1965)CrossRefGoogle Scholar
  7. 7.
    Kleinberg, J., Papadimitriou, C., Raghavan, P.: A Microeconomic View of Data Mining. Data Mining Knowledge Discovery 2, 311–324 (1998)CrossRefGoogle Scholar
  8. 8.
    Swapna Raj, P., Ravindran, B.: Utility Driven Clustering. In: 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS). AAAI Press, Florida (2011)Google Scholar
  9. 9.
    Tasi, C.-Y., Chiu, C.-C.: A purchase-based market segmentation methodology. Expert System Applications 27, 265–276 (2004)CrossRefGoogle Scholar
  10. 10.
    Cohn, D., Caruana, R., Mccallum, A.: Semi-supervised clustering with user feedback. Technical report (2003)Google Scholar

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

Personalised recommendations