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Incremental maintenance of discovered fuzzy association rules

Author

Listed:
  • A. Pérez-Alonso

    (Universidad Técnica Federico Santa María)

  • I. J. Blanco

    (University of Granada)

  • J. M. Serrano

    (University of Jaén)

  • L. M. González-González

    (University “Marta Abreu” of Las Villas)

Abstract

Fuzzy association rules (FARs) are a recognized model to study existing relations among data, commonly stored in data repositories. In real-world applications, transactions are continuously processed with upcoming new data, rendering the discovered rules information inexact or obsolete in a short time. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful for extracting knowledge in dynamic environments. However, executing the algorithms only to maintain previously discovered information creates inefficiencies in real-time decision support systems. In this paper, two active algorithms are proposed for incremental maintenance of previously discovered FARs, inspired by efficient methods for change computation. The application of a generic form of measures in these algorithms allows the maintenance of a wide number of metrics simultaneously. We also propose to compute data operations in real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain valuable information previously extracted, ready for decision making. Experimental results on education data and repository data sets show that our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.

Suggested Citation

  • A. Pérez-Alonso & I. J. Blanco & J. M. Serrano & L. M. González-González, 2021. "Incremental maintenance of discovered fuzzy association rules," Fuzzy Optimization and Decision Making, Springer, vol. 20(4), pages 429-449, December.
    • Handle: RePEc:spr:fuzodm:v:20:y:2021:i:4:d:10.1007_s10700-021-09350-3
    DOI: 10.1007/s10700-021-09350-3
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    References listed on IDEAS

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    1. Lenca, Philippe & Meyer, Patrick & Vaillant, Benoit & Lallich, Stephane, 2008. "On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid," European Journal of Operational Research, Elsevier, vol. 184(2), pages 610-626, January.
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