2023/04/26 by Théophile Berteloot, Richard Khoury, Berteloot, Théophile +3 · 1 citation
Computer Science · #Data Mining Algorithms and Applications #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.2304.13717
openalex publication_date 2023/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 are still no efficient way 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.