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Dependence Factor as a Rule Evaluation Measure

  • Marzena Kryszkiewicz
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 605)

Abstract

Certainty factor and lift are known evaluation measures of association rules. Nevertheless, they do not guarantee accurate evaluation of the strength of dependence between rule’s constituents. In particular, even if there is a strongest possible positive or negative dependence between rule’s constituents X and Y, these measures may reach values quite close to the values indicating independence of X and Y. Recently, we have proposed a new measure called a dependence factor to overcome this drawback. Unlike in the case of the certainty factor, when defining the dependence factor, we took into account the fact that for a given rule \(X \rightarrow Y\), the minimal conditional probability of the occurrence of Y given X may be greater than 0, while its maximal possible value may less than 1. In this paper, we first recall definitions and properties of all the three measures. Then, we examine the dependence factor from the point of view of an interestingness measure as well as we examine the relationship among the dependence factor for X and Y with those for \(\bar{X}\) and Y, X and \(\bar{Y}\), as well as \(\bar{X}\) and \(\bar{Y}\), respectively. As a result, we obtain a number of new properties of the dependence factor.

Notes

Acknowledgments

We wish to thank an anonymous reviewer for constructive comments, which influenced the final version of this paper positively.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

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