Formal Framework for the Study of Algorithmic Properties of Objective Interestingness Measures

  • Yannick Le Bras
  • Philippe Lenca
  • Stéphane Lallich
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 24)

Abstract

Association Rules Discovery is an increasing subdomain of Datamining. Many works have focused on the extraction and the evaluation of the association rules, leading to many technical improvments on the algorithms, and many different measures. But few number of them have tried to merge the both. We introduce here a formal framework for the study of association rules and interestingness measures that allows an analytic study of these objects. This framework is based on the contingency table of a rule and let us make a link between analytic properties of the measures and algorithmic properties. We give as example the case of three algorithmic properties for the extraction of association rules that were generalized and applied with the help of this framework. These properties allow a pruning of the search space based on a large number of measures and without any support constraint.

Keywords

Contingency Table Association Rule Descriptor System Mining Association Rule Pruning Strategy 
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 2012

Authors and Affiliations

  • Yannick Le Bras
    • 1
    • 3
  • Philippe Lenca
    • 1
    • 3
  • Stéphane Lallich
    • 2
  1. 1.Institut Telecom; Telecom Bretagne; UMR CNRS 3192 Lab-STICC, Technopôle Brest-IroiseBrest Cedex 3France
  2. 2.Laboratoire ERICUniversité de LyonLyon 2France
  3. 3.Université européenne de BretagneFrance

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