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Some aspects of rule discovery in data bases

  • Knowledge Discovery
  • Conference paper
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Information Systems and Data Management (CISMOD 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1006))

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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 ?’

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Author information

Authors and Affiliations

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

    L. Fleury, C Djeraba, H Briand & J Philippe

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

    L. Fleury & J Philippe

Authors
  1. L. Fleury
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  2. C Djeraba
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  3. H Briand
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  4. J Philippe
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Editor information

Subhash Bhalla

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© 1995 Springer-Verlag Berlin Heidelberg

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Fleury, L., Djeraba, C., Briand, H., Philippe, J. (1995). Some aspects of rule discovery in data bases. In: Bhalla, S. (eds) Information Systems and Data Management. CISMOD 1995. Lecture Notes in Computer Science, vol 1006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60584-3_32

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  • DOI: https://doi.org/10.1007/3-540-60584-3_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60584-3

  • Online ISBN: 978-3-540-47799-0

  • eBook Packages: Springer Book Archive

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