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Interestingness Measures for Fixed Consequent Rules

  • Jon Hills
  • Luke M. Davis
  • Anthony Bagnall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)

Abstract

Many different rule interestingness measures have been proposed in the literature; we show that, under two assumptions, at least twelve of these measures are proportional to Confidence. We consider rules with a fixed consequent, generated from a fixed data set. From these assumptions, we prove that Satisfaction, Ohsaki’s Conviction, Added Value, Brin’s Interest/Lift/Strength, Brin’s Conviction, Certainty Factor/Loevinger, Mutual Information, Interestingness, Sebag-Schonauer, Ganascia Index, Odd Multiplier, and Example/counter-example Rate are all monotonic with respect to Confidence. Hence, for ordering sets of partial classification rules with a fixed consequent, the Confidence measure is equivalent to any of the twelve other measures.

Keywords

Interestingness Rules Confidence Partial Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jon Hills
    • 1
  • Luke M. Davis
    • 1
  • Anthony Bagnall
    • 1
  1. 1.School of Computing SciencesUniversity of East AngliaNorwichUnited Kingdom

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