Combining CSP and Constraint-Based Mining for Pattern Discovery

  • Mehdi Khiari
  • Patrice Boizumault
  • Bruno Crémilleux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)


A well-known limitation of a lot of data mining methods is the huge number of patterns which are discovered: these large outputs hamper the individual and global analysis performed by the end-users of data. That is why discovering patterns of higher level is an active research field. In this paper, we investigate the relationship between local constraint-based mining and constraint satisfaction problems and we propose an approach to model and mine patterns combining several local patterns, i.e., patterns defined by n-ary constraints. The user specifies a set of n-ary constraints and a constraint solver generates the whole set of solutions. Our approach takes benefit from the recent progress on mining local patterns by pushing with a solver on local patterns all local constraints which can be inferred from the n-ary ones. This approach enables us to model in a flexible way any set of constraints combining several local patterns. Experiments show the feasibility of our approach.


Local Pattern Constraint Programming Mine Pattern Constraint Satisfaction Problem Local Constraint 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Apt, K.R., Wallace, M.: Constraint Logic Programming using Eclipse. Cambridge University Press, New York (2007)zbMATHGoogle Scholar
  2. 2.
    Benhamou, F., Goualard, F.: Universally quantified interval constraints. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 67–82. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Besson, J., Robardet, C., Boulicaut, J.-F.: Mining a new fault-tolerant pattern type as an alternative to formal concept discovery. In: 14th International Conference on Conceptual Structures (ICCS 2006), Aalborg, Denmark, pp. 144–157. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Bringmann, B., Zimmermann, A.: The chosen few: On identifying valuable patterns. In: Proceedings of the 12th IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, pp. 63–72 (2007)Google Scholar
  5. 5.
    Calders, T., Rigotti, C., Boulicaut, J.-F.: A survey on condensed representations for frequent sets. In: Boulicaut, J.-F., De Raedt, L., Mannila, H. (eds.) Constraint-Based Mining and Inductive Databases. LNCS (LNAI), vol. 3848, pp. 64–80. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Crémilleux, B., Soulet, A.: Discovering knowledge from local patterns with global constraints. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part II. LNCS, vol. 5073, pp. 1242–1257. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    De Raedt, L., Guns, T., Nijssen, S.: Constraint Programming for Itemset Mining. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 14th edn., Las Vegas, Nevada, USA (2008)Google Scholar
  8. 8.
    De Raedt, L.: A theory of inductive query answering. In: Proceedings of the IEEE Conference on Data Mining (ICDM 2002), Maebashi, Japan, 2002, pp. 123–130 (2002)Google Scholar
  9. 9.
    De Raedt, L., Zimmermann, A.: Constraint-based pattern set mining. In: Proceedings of the Seventh SIAM International Conference on Data Mining, Minneapolis, Minnesota, USA, April 2007, SIAM (2007)Google Scholar
  10. 10.
    ECLiPSe. Eclipse documentation,
  11. 11.
    Gecode Team. Gecode: Generic constraint development environment (2006),
  12. 12.
    Gervet, C.: Interval Propagation to Reason about Sets: Definition and Implementation of a Practical Language. Constraints 1(3), 191–244 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Giacometti, A., Miyaneh, E.K., Marcel, P., Soulet, A.: A framework for pattern-based global models. In: 10th Int. Conf. on Intelligent Data Engineering and Automated Learning, Burgos, Spain, pp. 433–440 (2009)Google Scholar
  14. 14.
    Hand, D.J.: ESF exploratory workshop on Pattern Detection and Discovery in Data Mining. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 1–12. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Kléma, J., Blachon, S., Soulet, A., Crémilleux, B., Gandrillon, O.: Constraint-based knowledge discovery from sage data. Silico Biology 8(0014) (2008)Google Scholar
  16. 16.
    Knobbe, A.: From local patterns to global models: The lego approach to data mining. In: International Workshop From Local Patterns to Global Models co-located with ECML/PKDD 2008, Antwerp, Belgium, September 2008, pp. 1–16 (2008)Google Scholar
  17. 17.
    Knobbe, A., Ho, E.: Pattern teams. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 577–584. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Lakshmanan, L.V., Ng, R., Hah, J., Pang, A.: Optimization of constrained frequent set queries with 2-variable constraints (1998)Google Scholar
  19. 19.
    Lhomme, O.: Consistency techniques for numeric csps. In: Proc. of the 13th IJCAI, Chambery, France, pp. 232–238 (1993)Google Scholar
  20. 20.
    Mamoulis, N., Stergiou, K.: Algorithms for quantified constraint satisfaction problems. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 752–756. Springer, Heidelberg (2004)Google Scholar
  21. 21.
    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
  22. 22.
    Moore, R.E.: Interval analysis. Prentice-Hall, Englewood Cliffs (1966)zbMATHGoogle Scholar
  23. 23.
    Ng, R.T., Lakshmanan, V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings of ACM SIGMOD 1998, pp. 13–24. ACM Press, New York (1998)CrossRefGoogle Scholar
  24. 24.
    Nijssen, S., Guns, T., De Raedt, L.: Correlated itemset mining in roc space: a constraint programming approach. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, France, June 2009, pp. 647–655 (2009)Google Scholar
  25. 25.
    Siebes, A., Vreeken, J., van Leeuwen, M.: Item sets that compress. In: Proceedings of the Sixth SIAM International Conference on Data Mining, Bethesda, MD, USA, April 2006, SIAM, Philadelphia (2006)Google Scholar
  26. 26.
    Soulet, A., Crémilleux, B.: An efficient framework for mining flexible constraints. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 661–671. Springer, Heidelberg (2005)Google Scholar
  27. 27.
    Soulet, A., Klema, J., Crémilleux, B.: Efficient Mining Under Rich Constraints Derived from Various Datasets. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 223–239. Springer, Heidelberg (2007)Google Scholar
  28. 28.
    Suzuki, E.: Undirected Discovery of Interesting Exception Rules. International Journal of Pattern Recognition and Artificial Intelligence 16(8), 1065–1086 (2002)CrossRefGoogle Scholar
  29. 29.
    Thornary, V., Gensel, J., Sherpa, P.: An hybrid representation for set constraint satisfaction problems. In: Workshop on Set Constraints co-located with the fourth Int. Conf. on Principles and Practice of Constraint Programming, Pisa, Italy (1998)Google Scholar
  30. 30.
    Yin, X., Han, J.: CPAR: classification based on predictive association rules. In: proceedings of the 2003 SIAM Int. Conf. on Data Mining (SDM 2003), San Fransisco, CA (May 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mehdi Khiari
    • 1
  • Patrice Boizumault
    • 1
  • Bruno Crémilleux
    • 1
  1. 1.Campus Côte de NacreGREYC, Université de Caen Basse-NormandieCaen CedexFrance

Personalised recommendations