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Mining top-k high average-utility itemsets based on breadth-first search

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Abstract

High average-utility itemset mining is a subfield of data mining that has extensive practical applications. However, it is difficult for users to determine a proper minimum threshold because they cannot accurately predict the number of patterns mined at a given threshold. To address this issue, top-k high average-utility itemset mining has been proposed where k is the number of high average-utility itemsets to be mined. In this paper, we design an effective algorithm (named ETAUIM) for finding top-k high average-utility itemsets. ETAUIM employs a breadth-first search strategy to efficiently explore the search space, and it utilizes a tighter upper bound instead of the average-utility upper bound to limit the search space. Additionally, ETAUIM removes irrelevant items during the mining process and utilizes an early abandoning strategy to terminate unnecessary join operations in advance. To evaluate the proposed algorithm, extensive experiments were conducted on six sparse datasets and two dense datasets. Four state-of-the-art algorithms were used for comparison. The experimental results show that ETAUIM has excellent performance and scalability. Moreover, ETAUIM always performs better for sparse datasets.

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Data availability

The data utilized in this study are available from the SPMF Open-Source Data Mining Library.

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Acknowledgements

This work is supported by the Natural Science Foundation of Zhejiang Province (LQ21F030010); Natural Science Foundation of Ningbo (202003N4306); the Public Welfare Foundation of Ningbo (2021S108); the Key Technology R&D Program of Ningbo (2022Z149); Ningbo Science and Technology Special Innovation Projects (2021Z079, 2022Z235).

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Authors and Affiliations

  1. School of Computer and Data Engineering, NingboTech University, Ningbo, 315100, Zhejiang, China

    Xuan Liu, Genlang Chen & Shiting Wen

  2. School of Advanced Technology, Xian Jiaotong-Liverpool University, Suzhou, 215028, Jiangsu, China

    Fangyu Wu

  3. School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, Zhejiang, China

    Wanli Zuo

Authors
  1. Xuan Liu
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  2. Genlang Chen
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  3. Fangyu Wu
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Contributions

Xuan Liu: Conceptualization, Methodology, Writing—original draft. Genlang Chen: Supervision, Project administration. Fangyu Wu: Data curation, Software. Shiting Wen: Validation, Writing—Review & Editing. Wanli Zuo: Writing—Review & Editing.

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Correspondence to Fangyu Wu.

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Liu, X., Chen, G., Wu, F. et al. Mining top-k high average-utility itemsets based on breadth-first search. Appl Intell 53, 29319–29337 (2023). https://doi.org/10.1007/s10489-023-05076-4

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