Constraint Selection for Semi-supervised Topological Clustering

  • Kais Allab
  • Khalid Benabdeslem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)


In this paper, we propose to adapt the batch version of self-organizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones.


Constrain selection semi-supervised clustering SOM 


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  1. 1.
    Basu, S., Davidson, I., Wagstaff, K.: Constrained clustering: Advances in algorithms, theory and applications. Chapman and Hall/CRC Data Mining and Knowledge Discovery Series (2008)Google Scholar
  2. 2.
    Frank, A., Asuncion, A.: Uci machine learning repository. Technical report, University of California (2010)Google Scholar
  3. 3.
    Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research 6, 937–965 (2005)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Clustering with instance level constraints. In: Proc. of the 18th International Conference on Machine Learning, pp. 577–584 (2001)Google Scholar
  5. 5.
    Lu, Z., Leen, T.K.: Semi-supervised learning with penalized probabilistic clustering. In: Advances in Neural information Processing Systems 17 (2005)Google Scholar
  6. 6.
    Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Clustering with instance level constraints. In: Proc. of the 17th International Conference on Machine Learning, pp. 1103–1110 (2000)Google Scholar
  7. 7.
    Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: theorical and empirical results. In: Proc. of ECML/PKDD, pp. 59–70 (2005)Google Scholar
  8. 8.
    Elghazel, H., Benabdeslem, K., Dussauchoy, A.: Constrained graph b-coloring based clustering approach. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 262–271. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Davidson, I., Ravi, S.S.: The complexity of non-hierarchical clustering with instance and cluster level constraints. Data Mining and Knowledge Discovery 61, 14–25 (2007)MathSciNetGoogle Scholar
  10. 10.
    Davidson, I., Ravi, S.S.: Clustering with constraints: feasibility issues and the k-means algorithm. In: Proc. of the SIAM International Conference on Data Mining, pp. 138–149 (2005)Google Scholar
  11. 11.
    Kulis, B., Basu, S., Dhillon, I., Mooney, R.: Semi-supervised graph clustering, a kernel approach. In: Proc. of the 22th International Conference on Machine Learning, pp. 577–584 (2005)Google Scholar
  12. 12.
    Davidson, I., Ester, M., Ravi, S.S.: Efficient incremental clustering with constraints. In: Proc. of 13th ACM Knowledge Discovery and Data Mining (2007)Google Scholar
  13. 13.
    Davidson, I., Wagstaff, K., Basu, S.: Measuring constraint-set utility for partitional clustering algorithms. In: Proc. of ECML/PKDD (2006)Google Scholar
  14. 14.
    Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proc. of the 21th International Conference on Machine Learning, pp. 11–18 (2004)Google Scholar
  15. 15.
    Kohonen, T.: Self organizing Map. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Herrmann, L., Ultsch, A.: Label propagation for semi-supervised learning in self-organizing maps. In: Proc. of the 6th WSOM (2007)Google Scholar
  17. 17.
    Belkin, M., Niyogi, P.: Using manifold structure for partially labelled classification. In: Proc. of Advances in Neural Information Processing Systems (2003)Google Scholar
  18. 18.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of COLT: Proc. of the Workshop on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  19. 19.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. The MIT Press, Cambridge (2006)CrossRefGoogle Scholar
  20. 20.
    Cheng, Y.: Convergence and ordering of kohonen’s batch map. Neural Computation 9(8), 1667–1676 (1997)CrossRefGoogle Scholar
  21. 21.
    Heskes, T., Kappen, B.: Error potentials for self-organization. In: Proc. of IEEE International Conference on Neural Networks, pp. 1219–1223 (1993)Google Scholar
  22. 22.
    Xing, E.P., Ng, A.Y., Jordan, M.I., Russel, S.: Distance metric learning, with application to clustering with side-information. Advances in Neural Information Processing Systems 15, 505–512 (2003)Google Scholar
  23. 23.
    Klein, D., Kamvar, S.D., Manning, C.D.: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Proc. of the 19th International Conference on Machine Learning, pp. 307–313 (2002)Google Scholar
  24. 24.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M., Downing, L.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 15 286(5439), 531–537 (1999)CrossRefGoogle Scholar
  25. 25.
    Ultsch, A.: Fundamental clustering problems suite (fcps). Technical report, University of Marburg (2005)Google Scholar
  26. 26.
    Vesanto, J., Alhoniemi, E.: Clustering of the self organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  27. 27.
    Kalyani, M., Sushmita, M.: Clustering and its validation in a symbolic framework. Pattern Recognition Letters 24(14), 2367–2376 (2003)CrossRefzbMATHGoogle Scholar
  28. 28.
    Rand, W.M.: Objective criteria for the evaluation of clustering method. Journal of the American Statistical Association 66, 846–850 (1971)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kais Allab
    • 1
  • Khalid Benabdeslem
    • 1
  1. 1.GAMA LaboratoryUniversity of Lyon1VilleurbanneFrance

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