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Data Mining and Knowledge Discovery

, Volume 10, Issue 1, pp 39–79 | Cite as

K-Optimal Rule Discovery

  • Geoffrey I. Webb
  • Songmao Zhang
Article

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.

exploratory rule discovery association rules classification rules rule search search space pruning 

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringMonash UniversityMelbourneAustralia
  2. 2.US National Library of Medicine (LHC/CgSB)National Institutes of HealthBethesdaUSA

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