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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)

Abstract

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 ?’

Keywords

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

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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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