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An efficient utility-list based high-utility itemset mining algorithm

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Abstract

High-utility itemset mining (HUIM) is an important task in data mining that can retrieve more meaningful and useful patterns for decision-making. One-phase HUIM algorithms based on the utility-list structure have been shown to be the most efficient as they can mine high-utility itemsets (HUIs) without generating candidates. However, storing itemset information for the utility-list is time-consuming and memory consuming. To address this problem, we propose an efficient simplified utility-list-based HUIM algorithm (HUIM-SU). In the proposed HUIM-SU algorithm, the simplified utility-list is proposed to obtain all HUIs effectively and reduce memory usage in the depth-first search process. Based on the the simplified utility-list, repeated pruning according to the transaction-weighted utilisation (TWU) reduces the number of items. In addition, a construction tree and compressed storage are introduced to further reduce the search space and the memory usage. The extension utility and itemset TWU are then proposed to be the upper bounds, which reduce the search space considerably. Extensive experimental results on dense and sparse datasets indicate that the proposed HUIM-SU algorithm is highly efficient in terms of the number of candidates, memory usage, and execution time.

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Acknowledgements

This work was supported in part by the National Key R&D Program of China under Grant 2017YFC1601800 and 2017YFC1601000, in part by the National Natural Science foundation of China under Grant 62073155, 62002137, 62106088, and 61673194, in part by “Blue Project” in Jiangsu Universities, China, in part by Guangdong Provincial Key Laboratory under Grant 2020B121201001, in part by Advanced Research Project of Specialty Leading Person in Higher Vocational Colleges in Jiangsu Province.

Funding

This work was supported in part by the National Key R&D Program of China under Grant 2017YFC1601000 and 2017YFC1601800, in part by the National Natural Science foundation of China, under Grant 62073155, 62106088, 61673194, and 61672263.

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Authors and Affiliations

  1. School of Internet of Things, Wuxi Institute of Technology, Gaolang Road, Wuxi, Jiangsu, 214121, China

    Zaihe Cheng

  2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Lihu Avenue, Wuxi, Jiangsu, 214122, China

    Zaihe Cheng, Wei Fang & Wei Shen

  3. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  4. Computer Science and Engineering Department, Southern University of Science and Technology, Shenzhen, China

    Bo Yuan

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  1. Zaihe Cheng
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  2. Wei Fang
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  3. Wei Shen
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  4. Jerry Chun-Wei Lin
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  5. Bo Yuan
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Contributions

Zaihe Cheng: Methodology. Wei Fang: Supervision. Wei Shen: Software. Writing- Original draft preparation. Jerry Chun-Wei Lin, Bo Yuan: Resources, English language.

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Correspondence to Wei Fang.

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Cheng, Z., Fang, W., Shen, W. et al. An efficient utility-list based high-utility itemset mining algorithm. Appl Intell 53, 6992–7006 (2023). https://doi.org/10.1007/s10489-022-03850-4

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