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A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures

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Abstract

In this paper, we first present an original and synthetic overview of the most commonly used association rule interestingness measures. These measures usually relate the confidence of a rule to an independence reference situation. Yet, some relate it to indetermination, or impose a minimum confidence threshold. We propose a systematic generalization of these measures, taking into account a reference point chosen by an expert in order to appreciate the confidence of a rule. This generalization introduces new connections between measures, and leads to the enhancement of some of them. Finally we propose new parameterized possibilities.

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Authors and Affiliations

  1. Laboratoire ERIC, Université Lyon 2, 5 Avenue Pierre Mendès-France, 69676, Bron Cedex, France

    Stéphane Lallich

  2. GET–ENST Bretagne–Département LUSSI, CNRS UMR 2872 TAMCIC, Technopôle de Brest Iroise, CS 83818, 29238, Brest Cedex, France

    Benoît Vaillant & Philippe Lenca

  3. UBS–IUT de Vannes–Département STID Laboratoire VALORIA, 8, rue Montaigne, BP 561, 56017, Vannes, France

    Benoît Vaillant

Authors
  1. Stéphane Lallich
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  2. Benoît Vaillant
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  3. Philippe Lenca
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Correspondence to Philippe Lenca.

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Lallich, S., Vaillant, B. & Lenca, P. A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures. Methodol Comput Appl Probab 9, 447–463 (2007). https://doi.org/10.1007/s11009-007-9025-7

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  • DOI: https://doi.org/10.1007/s11009-007-9025-7

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