An Efficient Framework for Mining Flexible Constraints

  • Arnaud Soulet
  • Bruno Crémilleux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3518)


Constraint-based mining is an active field of research which is a key point to get interactive and successful KDD processes. Nevertheless, usual solvers are limited to particular kinds of constraints because they rely on properties to prune the search space which are incompatible together. In this paper, we provide a general framework dedicated to a large set of constraints described by SQL-like and syntactic primitives. This set of constraints covers the usual classes and introduces new tough and flexible constraints. We define a pruning operator which prunes the search space by automatically taking into account the characteristics of the constraint at hand. Finally, we propose an algorithm which efficiently makes use of this framework. Experimental results highlight that usual and new complex constraints can be mined in large datasets.


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  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp. 432–444 (1994)Google Scholar
  2. 2.
    Bonchi, F., Lucchese, C.: On closed constrained frequent pattern mining. In: Proceedings of ICDM 2004, pp. 35–42 (2004)Google Scholar
  3. 3.
    Boulicaut, J.F., Bykowski, A., Rigotti, C.: Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery journal 7(1), 5–22 (2003)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Calders, T., Goethals, B.: Minimal k-free representations of frequent sets. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 71–82. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    De Raedt, L., Jäger, M., Lee, S.D., Mannila, H.: A theory of inductive query answering. In: Proceedings of ICDM 2002, Maebashi, Japan, pp. 123–130 (2002)Google Scholar
  6. 6.
    Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Knowledge Discovery and Data Mining, pp. 43–52 (1999)Google Scholar
  7. 7.
    Gade, K., Wang, J., Karypis, G.: Efficient closed pattern mining in the presence of tough block constraints. In: Proceedings of ACM SIGKDD, pp. 138–147 (2004)Google Scholar
  8. 8.
    Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. In: Communication of the ACM, pp. 58–64 (1996)Google Scholar
  9. 9.
    Jeudy, B., Rioult, F.: Database transposition for constrained (closed) pattern mining. In: Goethals, B., Siebes, A. (eds.) KDID 2004. LNCS, vol. 3377, pp. 89–107. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Kiefer, D., Gehrke, J., Bucila, C., White, W.: How to quickly find a witness. In: Proceedings of ACM SIGMOD/PODS 2003 Conference, pp. 272–283 (2003)Google Scholar
  11. 11.
    Kryszkiewicz, M.: Inferring knowledge from frequent patterns. In: Bustard, D.W., Liu, W., Sterritt, R. (eds.) Soft-Ware 2002. LNCS, vol. 2311, pp. 247–262. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)CrossRefGoogle Scholar
  13. 13.
    Ng, R.T., Lakshmanan, L.V.S., Han, J.: Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings of SIGMOD (1998)Google Scholar
  14. 14.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. LNCS. Springer, Heidelberg (1999)Google Scholar
  15. 15.
    Pei, J., Han, J., Lakshmanan, L.V.S.: Mining frequent item sets with convertible constraints. In: Proceedings of ICDE, pp. 433–442 (2001)Google Scholar
  16. 16.
    Soulet, A., Crémilleux, B., Rioult, F.: Condensed representation of emerging patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 127–132. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Soulet, A., Crémilleux, B.: A general framework designed for constraint-based mining. Technical report, Université de Caen, Caen, France (2004)Google Scholar
  18. 18.
    Wang, K., Jiang, Y., Yu, J.X., Dong, G., Han, J.: Pushing aggregate constraints by divide-and-approximate. In: Proceedings of ICDE, pp. 291–302 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Arnaud Soulet
    • 1
  • Bruno Crémilleux
    • 1
  1. 1.GREYC, CNRS – UMR 6072Université de CaenCaen CédexFrance

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