20.500.12556/DKUM-93255 NiaAutoARM: automated framework for constructing and evaluating association rule mining pipelines Numerical Association Rule Mining (NARM), which simultaneously handles both numerical and categorical attributes, is a powerful approach for uncovering meaningful associations in heterogeneous datasets. However, designing effective NARM solutions is a complex task involving multiple sequential steps, such as data preprocessing, algorithm selection, hyper-parameter tuning, and the definition of rule quality metrics, which together form a complete processing pipeline. In this paper, we introduce NiaAutoARM, a novel Automated Machine Learning (AutoML) framework that leverages stochastic population-based metaheuristics to automatically construct full association rule mining pipelines. Extensive experimental evaluation on ten benchmark datasets demonstrated that NiaAutoARM consistently identifies high-quality pipelines, improving both rule accuracy and interpretability compared to baseline configurations. Furthermore, NiaAutoARM achieves superior or comparable performance to the state-of-the-art VARDE algorithm while offering greater flexibility and automation. These results highlight the framework’s practical value for automating NARM tasks, reducing the need for manual tuning, and enabling broader adoption of association rule mining in real-world applications. AutoML association rule mining numerical association rule mining pipelines numerične asociacije rudarjenje cevovod asociacijsko pravilo true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/2227-7390/13/12/1957 © 2025 by the authors Članek v reviji 2025-06-16 11:33:43 2025-06-16 11:33:43 2025-06-18 07:16:32 0000-00-00 00:00:00 2025 0 0 20 str. iss. 12, [article no.] 1957 Vol. 13 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-08 2025-06-12 2025-06-13 004.4 239450883 10.3390/math13121957 2227-7390 mathematics-13-01957-v2.pdf mathematics-13-01957-v2.pdf 1 912E71833462EDA31A9B1B8E62B9EB57 c276655a36a783c90af3a8d4c1f5d8f4c3a278572095d7517bfd811efd6fe8f9 8218715f-4a95-11f0-a2f6-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=192254 https://www.mdpi.com/2227-7390/13/12/1957 0 5f64576d-4b40-11f0-9f54-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=192306 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0