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Mathematics
Volume 13
Issue 13
10.3390/math13132122
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Iztok Fister, Jr.
1,*,
Sancho Salcedo-Sanz
2,
Enrique Alexandre-Cortizo
2,
Damijan Novak
1,
Iztok Fister
1,
Vili Podgorelec
1 and
Mario Gorenjak
3
1
Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
2
Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain
3
Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(13), 2122; https://doi.org/10.3390/math13132122 (registering DOI)
Submission received: 4 June 2025 / Revised: 24 June 2025 / Accepted: 26 June 2025 / Published: 28 June 2025
(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)
Versions Notes

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.
Keywords: association rule mining; explainable artificial intelligence (XAI); numerical association rule mining; optimization algorithms association rule mining; explainable artificial intelligence (XAI); numerical association rule mining; optimization algorithms

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MDPI and ACS Style

Fister, I., Jr.; Salcedo-Sanz, S.; Alexandre-Cortizo, E.; Novak, D.; Fister, I.; Podgorelec, V.; Gorenjak, M. Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture. Mathematics 2025, 13, 2122. https://doi.org/10.3390/math13132122

AMA Style

Fister I Jr., Salcedo-Sanz S, Alexandre-Cortizo E, Novak D, Fister I, Podgorelec V, Gorenjak M. Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture. Mathematics. 2025; 13(13):2122. https://doi.org/10.3390/math13132122

Chicago/Turabian Style

Fister, Iztok, Jr., Sancho Salcedo-Sanz, Enrique Alexandre-Cortizo, Damijan Novak, Iztok Fister, Vili Podgorelec, and Mario Gorenjak. 2025. "Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture" Mathematics 13, no. 13: 2122. https://doi.org/10.3390/math13132122

APA Style

Fister, I., Jr., Salcedo-Sanz, S., Alexandre-Cortizo, E., Novak, D., Fister, I., Podgorelec, V., & Gorenjak, M. (2025). Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture. Mathematics, 13(13), 2122. https://doi.org/10.3390/math13132122

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