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Standardising the lift of an association rule

Author

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  • McNicholas, P.D.
  • Murphy, T.B.
  • O'Regan, M.

Abstract

The lift of an association rule is frequently used, both in itself and as a component in formulae, to gauge the interestingness of a rule. The range of values that lift may take is used to standardise lift so that it is more effective as a measure of interestingness. This standardisation is extended to account for minimum support and confidence thresholds. A method of visualising standardised lift, through the relationship between lift and its upper and lower bounds, is proposed. The application of standardised lift as a measure of interestingness is demonstrated on college application data and social questionnaire data. In the latter case, negations are introduced into the mining paradigm and an argument for this inclusion is put forward. This argument includes a quantification of the number of extra rules that arise when negations are considered.

Suggested Citation

  • McNicholas, P.D. & Murphy, T.B. & O'Regan, M., 2008. "Standardising the lift of an association rule," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4712-4721, June.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:10:p:4712-4721
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    References listed on IDEAS

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    1. Heike Hofmann & Adalbert Wilhelm, 2001. "Visual Comparison of Association Rules," Computational Statistics, Springer, vol. 16(3), pages 399-415, September.
    2. L. M. de Menezes & D. J. Bartholomew, 1996. "New Developments in Latent Structure Analysis Applied to Social Attitudes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 213-224, March.
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    Cited by:

    1. Jia Feng & Xiao-min Mu & Ling-ling Ma & Wei Wang, 2020. "Comorbidity Patterns of Older Lung Cancer Patients in Northeast China: An Association Rules Analysis Based on Electronic Medical Records," IJERPH, MDPI, vol. 17(23), pages 1-13, December.
    2. PRAET, Stiene & VAN AELST, Peter & MARTENS, David, 2018. "I like, therefore I am. Predictive modeling to gain insights in political preference in a multi-party system," Working Papers 2018014, University of Antwerp, Faculty of Business and Economics.
    3. Guo, Xin & Wang, David Z.W. & Wu, Jianjun & Sun, Huijun & Zhou, Li, 2020. "Mining commuting behavior of urban rail transit network by using association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).

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