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Title:Toward explainable time-series numerical association rule mining : a case study in smart-agriculture
Authors:ID Fister, Iztok (Author)
ID Salcedo-Sanz, Sancho (Author)
ID Alexandre-Cortizo, Enrique (Author)
ID Novak, Damijan (Author)
ID Fister, Iztok (Author)
ID Podgorelec, Vili (Author)
ID Gorenjak, Mario (Author)
Files:.pdf mathematics-13-02122-v2.pdf (329,69 KB)
MD5: 247DF64E65A2A8BA91C81D833E402E88
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
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
Publication status:Published
Publication version:Version of Record
Submitted for review:04.06.2025
Article acceptance date:26.06.2025
Publication date:28.06.2025
Publisher:MDPI
Year of publishing:2025
Number of pages:17 str.
Numbering:Vol. 13, iss. 13, [article no.] 2122
PID:20.500.12556/DKUM-94794 New window
UDC:004.8
ISSN on article:2227-7390
COBISS.SI-ID:246797827 New window
DOI:10.3390/math13132122 New window
Copyright:© 2025 by the authors
Publication date in DKUM:27.08.2025
Views:239
Downloads:12
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Mathematics
Shortened title:Mathematics
Publisher:MDPI AG
ISSN:2227-7390
COBISS.SI-ID:523267865 New window

Document is financed by a project

Funder:MICINN - Spanish Ministry of Science and Innovation
Project number:PID2023-150663NB-C21

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:asociacijsko pravilo rudarjenja, razložljiva umetna inteligenac, numerične asociacije, optimizacijski algoritmi


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