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Rule evaluations in a KDD system

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Book cover Database and Expert Systems Applications (DEXA 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 978))

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Abstract

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.

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Authors and Affiliations

  1. IRESTE, Nantes University La Chantrerie, CP 3003, 44087, Nantes cedex 03, France

    Laurent Fleury, Chabane Djeraba, Henri Briand & Jacque Philippe

  2. PerformanSe, 3, rue Racine, 44000, Nantes

    Laurent Fleury & Jacque Philippe

Authors
  1. Laurent Fleury
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  2. Chabane Djeraba
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  3. Henri Briand
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  4. Jacque Philippe
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Editor information

Norman Revell A Min Tjoa

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© 1995 Springer-Verlag Berlin Heidelberg

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Fleury, L., Djeraba, C., Briand, H., Philippe, J. (1995). Rule evaluations in a KDD system. In: Revell, N., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1995. Lecture Notes in Computer Science, vol 978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0049138

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  • DOI: https://doi.org/10.1007/BFb0049138

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60303-0

  • Online ISBN: 978-3-540-44790-0

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