K-Optimal Rule Discovery

  • Authors:
  • Geoffrey I. Webb;Songmao Zhang

  • Affiliations:
  • School of Computer Science and Software Engineering, Monash University, Melbourne, Australia 3800;US National Library of Medicine (LHC/CgSB), National Institutes of Health, Bethesda, USA 20894

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 2005

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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.