Abstract
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there is still no efficient way to mine association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.
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This research was made possible by the support of the INSPQ, as well as the financial support of the Canadian research funding agencies CIHR and NSERC.
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Berteloot, T., Khoury, R., Durand, A. (2025). Association Rules Mining with Auto-encoders. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15346. Springer, Cham. https://doi.org/10.1007/978-3-031-77731-8_5
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