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Two top-k HUIM algorithms based on the particle filter theory.

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Title: Two top-k HUIM algorithms based on the particle filter theory.
Authors: Yang, Yang1,2,3 (AUTHOR) p147450@siswa.ukm.edu.my, Sarim, Hafiz Mohd1 (AUTHOR) hms@ukm.edu.my, Wang, Honghai2,3 (AUTHOR) 524532949@qq.com
Source: Applied Intelligence. Nov2025, Vol. 55 Issue 17, p1-24. 24p.
Subjects: Data mining, Particle methods (Numerical analysis), Mathematical optimization
Abstract: Top-k high utility itemset mining(Top-k HUIM) has emerged as a critical research area, facilitating the discovery of valuable itemsets without predefined thresholds. Existing methods primarily focus on datasets without negative utilities, while approaches for handling negative utilities remain limited. Additionally, many top-k HUIM techniques require multiple global scans and large data structures, which hinder their efficiency and scalability. To address these challenges, we propose two novel algorithms: PFH (Particle Filter-based top-kHUIM for datasets without negative utilities) and PFHN (Particle Filter-based top-kHUIM for datasets with Negative utilities). PFH introduces a novel transmission process by assigning transition probabilities to particles for updating their states. A criterion for particle degeneration is proposed to terminate the transmission process, and a resampling strategy is employed to mitigate particle degeneration and improve algorithmic efficiency. In order to handle datasets with negative utilities, PFHN further introduces a utility flag filtering mechanism and employs a pruning strategy distinct from PFH to enhance efficiency. Extensive experiments demonstrate that both PFH and PFHN can efficiently and accurately mine top-kHUIs, providing a novel perspective for solving the top-k HUIM problem. [ABSTRACT FROM AUTHOR]
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Nov2025, Vol. 55 Issue 17, p1-24. 24p.
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  Data: Top-k high utility itemset mining(Top-k HUIM) has emerged as a critical research area, facilitating the discovery of valuable itemsets without predefined thresholds. Existing methods primarily focus on datasets without negative utilities, while approaches for handling negative utilities remain limited. Additionally, many top-k HUIM techniques require multiple global scans and large data structures, which hinder their efficiency and scalability. To address these challenges, we propose two novel algorithms: PFH (Particle Filter-based top-kHUIM for datasets without negative utilities) and PFHN (Particle Filter-based top-kHUIM for datasets with Negative utilities). PFH introduces a novel transmission process by assigning transition probabilities to particles for updating their states. A criterion for particle degeneration is proposed to terminate the transmission process, and a resampling strategy is employed to mitigate particle degeneration and improve algorithmic efficiency. In order to handle datasets with negative utilities, PFHN further introduces a utility flag filtering mechanism and employs a pruning strategy distinct from PFH to enhance efficiency. Extensive experiments demonstrate that both PFH and PFHN can efficiently and accurately mine top-kHUIs, providing a novel perspective for solving the top-k HUIM problem. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10489-025-06969-2
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      – SubjectFull: Particle methods (Numerical analysis)
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              Text: Nov2025
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