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Application of Bayesian Confirmation Measures for Mining Rules from Support-Confidence Pareto-Optimal Set

(Invited Paper)
  • Roman Slowinski
  • Izabela Brzezinska
  • Salvatore Greco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

We investigate a monotone link between Bayesian confirmation measures and rule support and confidence. In particular, we prove that two confirmation measures enjoying some desirable properties are monotonically dependent on at least one of the classic dimensions being rule support and confidence. As the confidence measure is unable to identify and eliminate non-interesting rules, for which a premise does not confirm a conclusion, we propose to substitute the confidence for one of the considered confirmation measures. We also provide general conclusions for the monotone link between any confirmation measure enjoying some desirable properties and rule support and confidence.

Keywords

Association Rule Total Order Frequent Itemsets Information Table Interestingness Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roman Slowinski
    • 1
    • 2
  • Izabela Brzezinska
    • 1
  • Salvatore Greco
    • 3
  1. 1.Institute of Computing Science Poznan University of TechnologyPoznanPoland
  2. 2.Institute for Systems Research, Polish Academy of SciencesWarsawPoland
  3. 3.Faculty of EconomicsUniversity of CataniaCataniaItaly

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