On Semantic Properties of Interestingness Measures for Extracting Rules from Data

  • Mondher Maddouri
  • Jamil Gammoudi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

The extraction of IF-THEN rules from data is a promising task of data mining including both Artificial Intelligence and Statistics. One of the difficulties encountered is how to evaluate the relevance of the extracted rules? Many authors use statistical interestingness measures to evaluate the relevance of each rule (taken alone). Recently, few research works have done a synthesis study of the existing interestingness measures but their study presents some limits. In this paper, firstly, we present an overview of related works studying more than forty interestingness measures. Secondly, we establish a list of nineteen other interestingness measures not referenced by the related works. Then, we identify twelve semantic properties characterizing the behavior of interestingness measures. Finally, we did a theoretical study of sixty two interestingness measures by outlining their semantic properties. The results of this study are useful to the users of a data-mining system in order to help them to choose an appropriate measure.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Mondher Maddouri
    • 1
  • Jamil Gammoudi
    • 2
  1. 1.Department of Maths & Computer Sciences, National Institute of Applied Sciences & Technology of Tunis – INSAT, University of Carthago, Centre Urbain Nord, B.P. 676, 1080 Tunis Cadex –Tunisia
  2. 2.Department of Computer Sciences, Faculty of Law, Economics and Management of Jendouba – FSJEG, University of Jendouba, Avenue de l’UMA - 8189 Jendouba –Tunisia

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