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A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures

Author

Listed:
  • Stéphane Lallich

    (Université Lyon 2)

  • Benoît Vaillant

    (GET–ENST Bretagne–Département LUSSI, CNRS UMR 2872 TAMCIC
    UBS–IUT de Vannes–Département STID Laboratoire VALORIA)

  • Philippe Lenca

    (GET–ENST Bretagne–Département LUSSI, CNRS UMR 2872 TAMCIC)

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.

Suggested Citation

  • Stéphane Lallich & Benoît Vaillant & Philippe Lenca, 2007. "A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures," Methodology and Computing in Applied Probability, Springer, vol. 9(3), pages 447-463, September.
    • Handle: RePEc:spr:metcap:v:9:y:2007:i:3:d:10.1007_s11009-007-9025-7
    DOI: 10.1007/s11009-007-9025-7
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    Cited by:

    1. Souhila Ghanem & Raphaël Couturier & Pablo Gregori, 2021. "An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote," Mathematics, MDPI, vol. 9(12), pages 1-12, June.

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