Rule evaluations in a KDD system

  • Laurent Fleury
  • Chabane Djeraba
  • Henri Briand
  • Jacque Philippe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 978)


In this paper, we address, some database problems that a knowledge discovery system deals with. In databases, data may be noisy (uncertain), sparse and redundant. To solve these problems, we describe two methods: The first one is the rule intensity measurement which is an index that answers the question ‘What is the probability of having a rule between two propositions or two conjunctions of propositions ?’ The intensity of rule enables us to measure the probability of an implication of the form: IF premise THEN Conclusion. This index seems to be adapted to the field of Knowledge Discovery in Databases (KDD). It resists noise, converges with the size of the sample, eliminates coarse rules, and can be used within the framework of an incremental algorithm. We will analyse it in detail, and compare it with others. The second one eliminates the redundant rules and superfluous propositions by using an algorithm for finding a minimal set of rules.


Knowledge Discovery in Databases evaluation measurement of an implication discovery in a noisy sparse and redundant context algorithm for finding a minimal set of rules 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Laurent Fleury
    • 1
    • 2
  • Chabane Djeraba
    • 1
  • Henri Briand
    • 1
  • Jacque Philippe
    • 1
    • 2
  1. 1.IRESTENantes University La ChantrerieNantes cedex 03France
  2. 2.PerformanSeNantes

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