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Mining Top-K constrained cross-level high-utility itemsets over data streams

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Abstract

Cross-Level High-Utility Itemsets Mining (CLHUIM) aims to discover interesting relationships between hierarchy levels by introducing the taxonomy of items. To tackle this issue of the current CLHUIM algorithms encountering a challenge in dealing with large search spaces, researchers have proposed the concept of mining Top-K cross-level high-utility itemsets(CLHUIs). However, the results obtained by these methods often contain redundant itemsets with significant differences in hierarchy levels, and a large proportion of itemsets with higher abstraction levels, making it neglect some detailed information and unable to provide information of itemsets within the specified hierarchy range. Additionally, they are unable to handle dynamic transactional data. To address the aforementioned problems, this paper proposes Top-K Constrained Cross-Level High-Utility Itemsets Mining (TKCCLHM) algorithm to efficiently mine Top-K itemsets across different hierarchy levels over data streams. Firstly, a new hierarchical level concept is introduced to control the abstraction level of the introduced items, and Top-K itemsets are mined within a specific hierarchy range based on this concept. Secondly, a sliding window-based data structure called Sliding Window-based Utility Projection List (SUPL) is designed, which combined with transaction projection techniques to mine CLHUIs efficiently. Lastly, a Batch and Utility Hash Table (BUHT) structure capable of storing batch and (generalized) item utility information is proposed, along with a new threshold raising strategy. Extensive experiments on six datasets with taxonomy information demonstrated that the proposed algorithm exhibited significant improvements in runtime and scalability performance compared to the state-of-the-art algorithms.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China (62062004) and the Ningxia Natural Science Foundation Project (2023AAC03315).

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Authors and Affiliations

  1. School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China

    Meng Han, Shujuan Liu, Zhihui Gao, Dongliang Mu & Ang Li

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  1. Meng Han
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  2. Shujuan Liu
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  3. Zhihui Gao
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  4. Dongliang Mu
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  5. Ang Li
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Contributions

MH contributed to writing—review & editing, supervision, funding acquisition, and resources. SL contributed to conceptualization, methodology, software, and writing—original draft. ZG contributed to data curation, investigation, and visualization. DM contributed to formal analysis and project administration. AL contributed to validation.

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Correspondence to Meng Han.

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Han, M., Liu, S., Gao, Z. et al. Mining Top-K constrained cross-level high-utility itemsets over data streams. Knowl Inf Syst 66, 2885–2924 (2024). https://doi.org/10.1007/s10115-023-02045-8

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  • DOI: https://doi.org/10.1007/s10115-023-02045-8

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