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A General Measure of Rule Interestingness

  • Szymon Jaroszewicz
  • Dan A. Simovici
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)

Abstract

The paper presents a new general measure of rule interestingness. Many known measures such as chi-square, gini gain or entropy gain can be obtained from this measure by setting some numerical parameters, including the amount of trust we have in the estimation of the probability distribution of the data. Moreover, we show that there is a continuum of measures having chi-square, Gini gain and entropy gain as boundary cases. Therefore our measure generalizes both conditional and unconditional classical measures of interestingness. Properties and experimental evaluation of the new measure are also presented.

Keywords

interestingness measure distribution Kullback-Leibler divergence Cziszar divergence rule 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Szymon Jaroszewicz
    • 1
  • Dan A. Simovici
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of Massachusetts at BostonBostonUSA

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