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Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture

Author

Listed:
  • Iztok Fister

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Sancho Salcedo-Sanz

    (Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain)

  • Enrique Alexandre-Cortizo

    (Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain)

  • Damijan Novak

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Iztok Fister

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Vili Podgorelec

    (Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia)

  • Mario Gorenjak

    (Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia)

Abstract

This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive.

Suggested Citation

  • Iztok Fister & Sancho Salcedo-Sanz & Enrique Alexandre-Cortizo & Damijan Novak & Iztok Fister & Vili Podgorelec & Mario Gorenjak, 2025. "Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture," Mathematics, MDPI, vol. 13(13), pages 1-17, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2122-:d:1690130
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