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Dung beetle optimization algorithm combined with off-policy reinforcement learning algorithm for the high-utility itemsets mining.

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Title: Dung beetle optimization algorithm combined with off-policy reinforcement learning algorithm for the high-utility itemsets mining.
Authors: Ma, Cuicui1 (AUTHOR), Han, Meng1 (AUTHOR) 2090511320@qq.com, Li, Yikai1 (AUTHOR), Xue, Yajie1 (AUTHOR), Ding, Jian1 (AUTHOR), Li, Juan1 (AUTHOR)
Source: Knowledge & Information Systems. 5/29/2026, Vol. 68 Issue 1, p1-63. 63p.
Subjects: Optimization algorithms, Reinforcement learning, Heuristic algorithms, Data mining, Clustering algorithms, Evolutionary algorithms
Abstract: High-utility itemsets mining aims to discover valuable itemsets from transactional data and is a key method in the field of data mining. Traditional exact algorithms face bottlenecks such as high computational costs and search space explosion when handling large-scale or complex data, while existing heuristic algorithms, although they have improved scalability to some extent, generally suffer from slow convergence, insufficient population diversity, and a tendency to get stuck in local optima. To address these issues, this study proposes a dung beetle optimization algorithm combined with off-policy reinforcement learning algorithm for the high-utility itemsets mining. To compress the search space and improve temporal and spatial efficiency, a reinforcement learning mechanism based on dynamic state clustering was designed. Breaking through the limitations of traditional methods that rely on single-state features, this study proposes a three-dimensional state fusion modeling and dynamic clustering strategy to achieve a refined characterization of search states. A multi-role collaborative evolution strategy based on beetle individuals was constructed, deeply coupling reinforcement learning with the evolutionary process to enhance global search capabilities and computational efficiency; simultaneously, a layered pruning and population repair strategy is proposed to enhance population diversity while suppressing premature convergence of the algorithm and reducing the loss of itemsets. Experimental results on multiple datasets demonstrate that, compared to current state-of-the-art algorithms, the proposed algorithm exhibits superior performance in terms of the completeness of high-utility itemsets, runtime, memory consumption, recall, and precision, while also demonstrating a faster convergence rate. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 194162876
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  Data: High-utility itemsets mining aims to discover valuable itemsets from transactional data and is a key method in the field of data mining. Traditional exact algorithms face bottlenecks such as high computational costs and search space explosion when handling large-scale or complex data, while existing heuristic algorithms, although they have improved scalability to some extent, generally suffer from slow convergence, insufficient population diversity, and a tendency to get stuck in local optima. To address these issues, this study proposes a dung beetle optimization algorithm combined with off-policy reinforcement learning algorithm for the high-utility itemsets mining. To compress the search space and improve temporal and spatial efficiency, a reinforcement learning mechanism based on dynamic state clustering was designed. Breaking through the limitations of traditional methods that rely on single-state features, this study proposes a three-dimensional state fusion modeling and dynamic clustering strategy to achieve a refined characterization of search states. A multi-role collaborative evolution strategy based on beetle individuals was constructed, deeply coupling reinforcement learning with the evolutionary process to enhance global search capabilities and computational efficiency; simultaneously, a layered pruning and population repair strategy is proposed to enhance population diversity while suppressing premature convergence of the algorithm and reducing the loss of itemsets. Experimental results on multiple datasets demonstrate that, compared to current state-of-the-art algorithms, the proposed algorithm exhibits superior performance in terms of the completeness of high-utility itemsets, runtime, memory consumption, recall, and precision, while also demonstrating a faster convergence rate. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 5/29/2026
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