20.500.12556/DKUM-94794 Toward explainable time-series numerical association rule mining a case study in smart-agriculture 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. association rule mining explainable artificial intelligence XAI numerical association rule mining optimization algorithms asociacijsko pravilo rudarjenja razložljiva umetna inteligenac numerične asociacije optimizacijski algoritmi true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/mathematics © 2025 by the authors Članek v reviji 2025-08-27 14:06:17 2025-08-27 14:06:17 2025-08-30 03:25:08 0000-00-00 00:00:00 2025 0 0 17 str. iss. 13, [article no.] 2122 Vol. 13 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-06-04 2025-06-26 2025-06-28 004.8 246797827 10.3390/math13132122 2227-7390 mathematics-13-02122-v2.pdf mathematics-13-02122-v2.pdf 1 247DF64E65A2A8BA91C81D833E402E88 85c33300bb704cb46fdf2420925d546fb3e09a54abc3c4d91888e09a427b5144 fba1711f-833e-11f0-8e8c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=196626 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0