Assessing the quality of rules with a new monotonic interestingness measure z

Salvatore Greco, R. Slowinski, I. Szczech

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


    The development of effective interestingness measures that help in interpretation and evaluation of the discovered knowledge is an active research area in data mining and machine learning. In this paper, we consider a new Bayesian confirmation measure for "if..., then..." rules proposed in [4]. We analyze this measure, called Z, with respect to valuable property M of monotonic dependency on the number of objects in the dataset satisfying or not the premise or the conclusion of the rule. The obtained results unveil interesting relationship between Z measure and two other simple and commonly used measures of rule support and anti-support, which leads to efficiency gains while searching for the best rules.
    Original languageEnglish
    Title of host publicationArtificial intelligence and soft computing: proceedings of the 9th international conference
    EditorsL. Rutkowski, R. Tadeusiewicz, L. Zadeh, J. Zurada
    Place of PublicationBerlin
    Number of pages10
    ISBN (Print)9783540695721
    Publication statusPublished - 2008

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743


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