Towards Constrained Co-clustering in Ordered 0/1 Data Sets

  • Ruggero G. Pensa
  • Céline Robardet
  • Jean-François Boulicaut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


Within 0/1 data, co-clustering provides a collection of bi-clusters, i.e., linked clusters for both objects and Boolean properties. Beside the classical need for grouping quality optimization, one can also use user-defined constraints to capture subjective interestingness aspects and thus to improve bi-cluster relevancy. We consider the case of 0/1 data where at least one dimension is ordered, e.g., objects denotes time points, and we introduce co-clustering constrained by interval constraints. Exploiting such constraints during the intrinsically heuristic clustering process is challenging. We propose one major step in this direction where bi-clusters are computed from collections of local patterns. We provide an experimental validation on two temporal gene expression data sets.


Local Pattern Rand Index Gene Expression Data Analysis Interval Constraint Boolean Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Robardet, C., Feschet, F.: Efficient local search in conceptual clustering. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 323–335. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings ACM SIGKDD 2003, Washington, USA, pp. 89–98. ACM Press, New York (2003)Google Scholar
  3. 3.
    Ritschard, G., Zighed, D.A.: Simultaneous row and column partitioning: Evaluation of a heuristic. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 468–472. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Trans. Comput. Biol. Bioinf. 1(1), 24–45 (2004)CrossRefGoogle Scholar
  5. 5.
    Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Proceedings ICML 2001, Williamstown, USA, pp. 577–584. Morgan Kaufmann, San Francisco (2001)Google Scholar
  6. 6.
    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: Proceedings ICML 2002, Sydney, Australia, pp. 307–314. Morgan Kaufmann, San Francisco (2002)Google Scholar
  7. 7.
    Davidson, I., Ravi, S.S.: Clustering with constraints: Feasibility issues and the k-means algorithm. In: Proceedings SIAM SDM 2005, Newport Beach, USA (2005)Google Scholar
  8. 8.
    Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 59–70. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Pensa, R.G., Robardet, C., Boulicaut, J.-F.: A bi-clustering framework for categorical data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 643–650. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Sese, J., Kurokawa, Y., Monden, M., Kato, K., Morishita, S.: Constrained clusters of gene expression profiles with pathological features. Bioinformatics 20(17), 3137–3145 (2004)CrossRefGoogle Scholar
  11. 11.
    Besson, J., Robardet, C., Boulicaut, J.-F., Rome, S.: Constraint-based concept mining and its application to microarray data analysis. Intelligent Data Analysis 9(1), 59–82 (2005)Google Scholar
  12. 12.
    Goodman, L.A., Kruskal, W.H.: Measures of association for cross classification. Journal of the American Statistical Association 49, 732–764 (1954)zbMATHCrossRefGoogle Scholar
  13. 13.
    Bozdech, Z., Llinás, M., Pulliam, B.L., Wong, E.D., Zhu, J., DeRisi, J.L.: The transcriptome of the intraerythrocytic developmental cycle of plasmodium falciparum. PLoS Biology 1(1), 1–16 (2003)CrossRefGoogle Scholar
  14. 14.
    Arbeitman, M., Furlong, E., Imam, F., Johnson, E., Null, B., Baker, B., Krasnow, M., Scott, M., Davis, R., White, K.: Gene expression during the life cycle of drosophila melanogaster. Science 297, 2270–2275 (2002)CrossRefGoogle Scholar
  15. 15.
    Becquet, C., Blachon, S., Jeudy, B., Boulicaut, J.-F., Gandrillon, O.: Strong association rule mining for large gene expression data analysis: a case study on human SAGE data. Genome Biology 12 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruggero G. Pensa
    • 1
  • Céline Robardet
    • 1
  • Jean-François Boulicaut
    • 1
  1. 1.LIRIS CNRS UMR 5205INSA LyonVilleurbanneFrance

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