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FHUQI-Miner: Fast high utility quantitative itemset mining

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Abstract

High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task is useful to understand customer behavior, an important limitation is that high utility itemsets do not provide information about the purchase quantities of items. Recently, some algorithms were designed to address this issue by finding quantitative high utility itemsets but they can have very long execution times due to the larger search space. This paper addresses this issue by proposing a novel efficient algorithm for high utility quantitative itemset mining, called FHUQI-Miner (Fast High Utility Quantitative Itemset Miner). It performs a depth-first search and adopts two novel search space reduction strategies, named Exact Q-items Co-occurrence Pruning Strategy (EQCPS) and Range Q-items Co-occurrence Pruning Strategy (RQCPS). Experimental results show that the proposed algorithm is much faster than the state-of-art HUQI-Miner algorithm on sparse datasets.

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Authors and Affiliations

  1. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Mourad Nouioua & Philippe Fournier-Viger

  2. University of Bordj Bou Arreridj, Bordj Bou Arreridj, Algeria

    Mourad Nouioua

  3. Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan

    Cheng-Wei Wu

  4. Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

  5. College of Information Science and Technology, Jinan University, Guangzhou, China

    Wensheng Gan

Authors
  1. Mourad Nouioua
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  2. Philippe Fournier-Viger
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  3. Cheng-Wei Wu
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  4. Jerry Chun-Wei Lin
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  5. Wensheng Gan
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Correspondence to Philippe Fournier-Viger.

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Nouioua, M., Fournier-Viger, P., Wu, CW. et al. FHUQI-Miner: Fast high utility quantitative itemset mining. Appl Intell 51, 6785–6809 (2021). https://doi.org/10.1007/s10489-021-02204-w

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  • DOI: https://doi.org/10.1007/s10489-021-02204-w

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