Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture
Iztok Fister, Sancho Salcedo-Sanz, Enrique Alexandre-Cortizo, Damijan Novak, Iztok Fister, Vili Podgorelec, Mario Gorenjak
Source 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.
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