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Incremental maintenance of discovered fuzzy association rules

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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.

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Acknowledgements

The authors would like to thank the members of the Iberoamerican Association of Postgraduate Universities (AUIP) for their international academic mobility program. We also thank our colleagues from the IDBIS Research Group (Intelligent DataBases and Information Systems) who provided insight and expertise that greatly assisted the research. We are grateful to all people who have contributed with their suggestions for improving the final version of the manuscript.

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Authors and Affiliations

  1. Department of Electronics and Informatics, Universidad Técnica Federico Santa María, 4030000, Concepción, Chile

    A. Pérez-Alonso

  2. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    I. J. Blanco

  3. Department of Computer Science, University of Jaén, 23071, Jaén, Spain

    J. M. Serrano

  4. Department of Computer Science, University “Marta Abreu” of Las Villas, 54830, Santa Clara, Cuba

    L. M. González-González

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  1. A. Pérez-Alonso
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  3. J. M. Serrano
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  4. L. M. González-González
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Correspondence to A. Pérez-Alonso.

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Pérez-Alonso, A., Blanco, I.J., Serrano, J.M. et al. Incremental maintenance of discovered fuzzy association rules. Fuzzy Optim Decis Making 20, 429–449 (2021). https://doi.org/10.1007/s10700-021-09350-3

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