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\(\mathcal{IGB}\): A New Informative Generic Base of Association Rules

  • Gh. Gasmi
  • S. Ben Yahia
  • E. Mephu Nguifo
  • Y. Slimani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3518)

Abstract

The problem of the relevance and the usefulness of extracted association rules is becoming paramount, since an overwhelming number of association rules may be derived from even reasonably sized real-life databases. A possible solution consists in using results of Formal Concept Analysis to generate a generic base of association rules. This set, of reduced size, makes it possible to derive all the association rules via an adequate axiomatic system. In this paper, we introduce a novel generic and informative base of association rules, conveying two types of knowledge: “factual” and “implicative”. We present also a valid and complete axiomatic system allowing to derive the set of all association rules. Results of the experiments carried out on real-life databases showed important profits in terms of compactness of the introduced generic base.

Keywords

Association rules Generic base Galois connection Axio-matic system 

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References

  1. 1.
    Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  2. 2.
    Pasquier, N., Bastide, Y., Touil, R., Lakhal, L.: Pruning closed itemset lattices for association rules. In: Bouzeghoub, M. (ed.) Proceedings of 14th Intl. Conference Bases de Données Avancées, Hammamet, Tunisia, pp. 177–196 (1998)Google Scholar
  3. 3.
    Bastide, Y., Pasquier, N., Taouil, R., Lakhal, L., Stumme, G.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Proceedings of the Intl. Conference DOOD 2000. LNCS, pp. 972–986. Springer, Heidelberg (2000)Google Scholar
  4. 4.
    Godin, R., Mineau, G.W., Missaoui, R., Mili, H.: Méthodes de Classification Conceptuelle Basées sur le Treillis de Galois et Applications. Revue d’intelligence Artificielle 9, 105–137 (1995)Google Scholar
  5. 5.
    Liquière, M., Nguifo, E.M.: Legal (learning with galois lattice): Un système d’apprentisage de concepts à partir d’exemples. In: Proceedings of the Intl. 5th Journées Francaises de l’apprentissage, Lannion, France, pp. 93–114 (1990)Google Scholar
  6. 6.
    BenYahia, S., Nguifo, E.M.: Approches d’extraction de règles d’association basées sur la correspondance de galois. Ingénierie des Systèmes d’Information (ISI), Hermès-Lavoisier 3–4, 23–55 (2004)CrossRefGoogle Scholar
  7. 7.
    Tan, P., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ICDM 2002), pp. 32–41. ACM Press, New York (2002)CrossRefGoogle Scholar
  8. 8.
    Kryszkiewicz, M.: Representative association rules. In: Research and Development in Knowledge Discovery and Data Mining. In: Proc. of Second Pacific-Asia Conference (PAKDD), Melbourne, Australia, pp. 198–209 (1998)Google Scholar
  9. 9.
    Luong, V.P.: Raisonnement sur les règles d’association. In: Proceedings 17ème Journées Bases de Données Avancées BDA 2001, Agadir (Maroc), Cépaduès Edition, pp. 299–310 (2001)Google Scholar
  10. 10.
    Kryszkiewicz, M.: Concise representations of association rules. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 92–109. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Guigues, J., Duquenne, V.: Familles minimales d’implications informatives résultant d’un tableau de données binaires. Mathématiques et Sciences Humaines, 5–18 (1986)Google Scholar
  12. 12.
    Luxenburger, M.: Implication partielles dans un contexte. Mathématiques et Sciences Humaines 29, 35–55 (1991)MathSciNetGoogle Scholar
  13. 13.
    BenYahia, S., Nguifo, E.M.: Revisiting generic bases of association rules. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 58–67. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    BenYahia, S., Nguifo, E.M.: Emulating a cooperative behavior in a generic association rule visualization tool. In: Proceedings of 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), Boca Raton, Florida, pp. 148–155 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Gh. Gasmi
    • 1
  • S. Ben Yahia
    • 1
    • 2
  • E. Mephu Nguifo
    • 2
  • Y. Slimani
    • 1
  1. 1.Départment des Sciences de l’InformatiqueFaculté des Sciences de TunisTunisTunisie
  2. 2.Centre de Recherche en Informatique de Lens-IUT de LensLens Cedex

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