Assessing the Quality of Rules with a New Monotonic Interestingness Measure Z

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5097)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Faculty of EconomicsUniversity of CataniaCataniaItaly
  2. 2.Institute of Computing SciencePoznan University of TechnologyPoznanPoland
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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