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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/12/1957 |
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| author | Uroš Mlakar Iztok Fister Iztok Fister |
| author_facet | Uroš Mlakar Iztok Fister Iztok Fister |
| author_sort | Uroš Mlakar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d58521bbe04a4ce79de0102a7e788cef |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-d58521bbe04a4ce79de0102a7e788cef2025-08-20T03:16:34ZengMDPI AGMathematics2227-73902025-06-011312195710.3390/math13121957NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining PipelinesUroš Mlakar0Iztok Fister1Iztok Fister2Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, SloveniaNumerical 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.https://www.mdpi.com/2227-7390/13/12/1957AutoMLassociation rule miningnumerical association rule miningpipelines |
| spellingShingle | Uroš Mlakar Iztok Fister Iztok Fister NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines Mathematics AutoML association rule mining numerical association rule mining pipelines |
| title | NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines |
| title_full | NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines |
| title_fullStr | NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines |
| title_full_unstemmed | NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines |
| title_short | NiaAutoARM: Automated Framework for Constructing and Evaluating Association Rule Mining Pipelines |
| title_sort | niaautoarm automated framework for constructing and evaluating association rule mining pipelines |
| topic | AutoML association rule mining numerical association rule mining pipelines |
| url | https://www.mdpi.com/2227-7390/13/12/1957 |
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