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K-Optimal Rule Discovery

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Abstract

K-optimal rule discovery finds the K rules that optimize a user-specified measure of rule value with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of K-optimal rule discovery tasks and demonstrates its efficiency.

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Authors and Affiliations

  1. School of Computer Science and Software Engineering, Monash University, P.O. Box 75, Melbourne, Victoria, 3800, Australia

    Geoffrey I. Webb

  2. US National Library of Medicine (LHC/CgSB), National Institutes of Health, 8600 Rockville Pike, MS 43 (Bldg 38A, B1N28T), Bethesda, MD, 20894, USA

    Songmao Zhang

Authors
  1. Geoffrey I. Webb
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  2. Songmao Zhang
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Correspondence to Geoffrey I. Webb or Songmao Zhang.

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Webb, G., Zhang, S. K-Optimal Rule Discovery. Data Min Knowl Disc 10, 39–79 (2005). https://doi.org/10.1007/s10618-005-0255-4

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  • DOI: https://doi.org/10.1007/s10618-005-0255-4

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