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CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets

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Abstract

The identification of both closed frequent high average utility itemsets (CFHAUIs) and generators of frequent high average utility itemsets (GFHAUIs) has substantial significance because they play an essential and concise role in representing frequent high average utility itemsets (FHAUIs). These concise summaries offer a compact yet crucial overview that can be much smaller. In addition, they allow the generation of non-redundant high average utility association rules, a crucial factor for decision-makers to consider. However, difficulty arises from the complexity of discovering these representations, primarily because the average utility function does not satisfy both monotonic and anti-monotonic properties within each equivalence class, that is for itemsets sharing the same subset of transactions. To tackle this challenge, this paper proposes an innovative method for efficiently extracting CFHAUIs and GFHAUIs. This approach introduces novel bounds on the average utility, including a weak lower bound called \(wlbau\) and a lower bound named \(auvlb\). Efficient pruning strategies are also designed with the aim of early elimination of non-closed and/or non-generator FHAUIs based on the \(wlbau\) and \(auvlb\) bounds, leading to quicker execution and lower memory consumption. Additionally, the paper introduces a novel algorithm, CG-FHAUI, designed to concurrently discover both GFHAUIs and CFHAUIs. Empirical results highlight the superior performance of the proposed algorithm in terms of runtime, memory usage, and scalability when compared to a baseline algorithm.

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Data availability

The manuscript has associated data in a data repository. [Author’s comment: The datasets used to evaluate algorithms and the synthetic dataset generator used in this study, are public and were obtained from [43]. The generated synthetic datasets will be shared on request.]

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Acknowledgements

Hai Duong was funded by the Postdoctoral Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2023.STS.52.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam

    Hai Duong & Bac Le

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Hai Duong & Bac Le

  3. Department of Mathematics and Computer Science, Dalat University, Dalat, Vietnam

    Hai Duong & Tin Truong

  4. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

Authors
  1. Hai Duong
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  2. Tin Truong
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  3. Bac Le
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  4. Philippe Fournier-Viger
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Contributions

HD, TT and BL contributed equally to the study conception and design. Material preparation, data collection and analysis were performed by HD, TT, and BL. The first draft of the manuscript was written by HD, TT, BL and PF-V. All authors read and approved the content of the manuscript.

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Correspondence to Hai Duong.

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The authors have no conflict of interest. This research was carried using public data. No experiments were conducted with humans or animals.

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Duong, H., Truong, T., Le, B. et al. CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02121-7

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