A Unified View of Objective Interestingness Measures

  • Céline Hébert
  • Bruno Crémilleux
Conference paper

DOI: 10.1007/978-3-540-73499-4_40

Volume 4571 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Hébert C., Crémilleux B. (2007) A Unified View of Objective Interestingness Measures. In: Perner P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science, vol 4571. Springer, Berlin, Heidelberg

Abstract

Association rule mining often results in an overwhelming number of rules. In practice, it is difficult for the final user to select the most relevant rules. In order to tackle this problem, various interestingness measures were proposed. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conflicting information. In this paper, we give a unified view of objective interestingness measures. We define a new framework embedding a large set of measures called SBMs and we prove that the SBMs have a similar behavior. Furthermore, we identify the whole collection of the rules simultaneously optimizing all the SBMs. We provide an algorithm to efficiently mine a reduced set of rules among the rules optimizing all the SBMs. Experiments on real datasets highlight the characteristics of such rules.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Céline Hébert
    • 1
  • Bruno Crémilleux
    • 1
  1. 1.GREYC, CNRS - UMR 6072, Université de Caen, Campus Côte de Nacre, F-14032 Caen CédexFrance