Some aspects of rule discovery in data bases

  • L. Fleury
  • C Djeraba
  • H Briand
  • J Philippe
Knowledge Discovery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1006)


Rule Discovery in Databases integrates machine learning, probabilistic techniques and database concepts to learn a range comprehensible knowledge in sparse, noisy and redundant data. The discovery enables the learning of rules from data and extract their underlying structure. In this paper, we present the probabilistic index and the notion of minimal set of discovered rules which enhance runtime performance, improve discovery accuracy, resist noise, converges with the size of the sample, and eliminates coarse and redundant rules. This index can be used within the framework of an incremental discovery system. In other words, in this paper, we describe the rule intensity measurement which is an index that answers the question ‘What is the probability of having a rule of the form ‘IF premise THEN Conclusion’; the premise and conclusion are conjunctions of propositions ?’


Knowledge Discovery in Databases discovered rules rule intensity measurement noisy sparsity redundancy 


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  1. [Anw92]
    T. M. Anwar, H. W. Beck, S. B. Navathe. “Knowledge Mining by Imprecise Querying: A Classification-Based Approach, “IEEE 8 th Int. Conf. on Data Eng. Phoenix, Arizona, Feb. 1992.Google Scholar
  2. [Bri 86]
    Briand H., Crampes J. B., Hebrail Y., Herin-Aime D., Kouloumdjan, Sabatier R., “Les systemes d'Information”, DUNOD edition, 1986.Google Scholar
  3. [Cla 88]
    Clark P., Niblett T. “The NC2 induction algorithm. Machine Learning”, 3. p 261–283 1988.Google Scholar
  4. [Cen 87]
    Cendrowska J. “An Algorithm for inducing modular rules”, Int J. Man-Machine Studies, p 349–370, 1987.Google Scholar
  5. [Did 91]
    Diday E. Mennessier M.O. “Analyse symbolique pour la prévision de séries chronologiques pseudo-périodiques”, Induction symbolique et numérique à partir de données, p 179–192,Cépaduès-éditions, 91.Google Scholar
  6. [Fra 91]
    Frawley W. J., Piatetsky-Shapiro, C. J. Matheus, “Knowledge discovery in databases: an overview”, in Knowledge Dicovery in Databases. Cambridge, MA: AAAI/MIT, 1991, pages 1–27.Google Scholar
  7. [Gan 88]
    Ganascia J. G., “CHARADE: A rule system learning intelligence”, IJCAI, Milan, Italy, Août 1987.Google Scholar
  8. [Goo 89]
    R. M. Goodman, P. Smyth, “The induction of probabilistic rules set — the rule algorithm”, Proceedings of the sixth international workshop on machine learning, Splatz B. ed., p 129–132, San Mateo, CA Morgan Kaufmann 1989.Google Scholar
  9. [Gra 93]
    Gras R., Larher A. “L'implication statistique, une nouvelle méthode d'analyse de données”, Mathématiques, Informatique et Sciences Humaines n∘120.Google Scholar
  10. [Bao 91]
    Ho Tu Bao, Tong Thi Thanh Huyen, “A method for generating rules from examples and its application”, Symbolic Numeric Data Analysis And Learning, p 493–504, Nova Sciences Publishers 1991.Google Scholar
  11. [Kod 93]
    Kodrattof Y., Tecuci G., “Techniques of design and DISCIPLE learning apprentice”, Knowledge acquisition and learning, Kaufmann edition, pages 655–668, 1993.Google Scholar
  12. [Klo 91]
    W. Klosgen, “Visualization and adaptivity in the statics interpreter EXPLORA”, in Workshop Notes from the 9th Nat. Conf. Art. Intell.: Knowledge Discovery in Databases. American Association for Artificial Intelligence, Anaheim, CA, July 1991, pages 25–34.Google Scholar
  13. [Mat 93]
    C. J. Matheus, P. K. Chan, G. Piatetsky-Shapiro, “Systems for Knowledge Discovery in Databases”, IEEE Trans. Knowl. Data Eng., vol 5, n 6, 1993.Google Scholar
  14. [Qui 87]
    J. R. Quinlan,“Generating Production Rules from Decisions Trees”. The 10 th International Conference on Artificial Intelligence, p 304–307, 1987.Google Scholar
  15. [Seb 91]
    M. Sebag, M. Schoenauer, “Un réseau de règles d'apprentissage”, Induction symbolique et numérique à partir de données, p 241–255, Cépaduès-éditions, 91.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • L. Fleury
    • 1
    • 2
  • C Djeraba
    • 1
  • H Briand
    • 1
  • J Philippe
    • 1
    • 2
  1. 1.IRESTENantes UniversityNantes cedex 03France
  2. 2.PerformanSeNantes

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