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<p>With a user-specified minimum utility threshold (<i>minutil</i>), periodic high-utility pattern mining (PHUPM) aims to identify high-utility patterns that occur periodically in a transaction database. A pattern is deemed periodic if its period aligns with the periodicity constraint set by the user. However, users may not be interested in all periodic high-utility patterns (PHUPs). Moreover, setting <i>minutil</i> in advance is also a challenging issue. To address these issues, our research introduces an algorithm called TPU for extracting the most significant top-<i>k</i> periodic and high-utility patterns that may or may not include negative utility values. This TPU algorithm utilizes positive and negative utility lists (PNUL) and period-estimated utility co-occurrence structure (PEUCS) to store pertinent itemset information. Additionally, it incorporates the periodic real item utility (PIU), periodic co-occurrence utility descending (PCUD), and periodic real utility (PRU) threshold-raising strategies to elevate the thresholds rapidly. By using the proposed threshold-raising strategies, the runtime was reduced by approximately 5% on the datasets used in the experiments. Specifically, the runtime was reduced by up to 50% on the mushroom_negative and kosarak_negative datasets, and by up to 10% on the chess_negative dataset. Memory consumption was reduced by about 2%, with the largest reduction of about 30% observed on the mushroom_negative dataset. Through extensive experiments, we have demonstrated that our algorithm can accurately and effectively extract the top-<i>k</i> periodic high-utility patterns. This paper successfully addresses the top-<i>k</i> mining issue and contributes to data science. Furthermore, the applications of the proposed algorithm in engineering include data mining, expert systems, and web intelligence in various fields, such as smart retail, cyberspace security, and risk prediction. The code and datasets are publicly available at <a href="https://github.com/DSI-Lab1/TPU">https://github.com/DSI-Lab1/TPU</a>.</p>

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Discovering top-k periodic and high-utility patterns

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Qingfeng Zhou,
  • Wensheng Gan,
  • Guoting Chen

摘要

With a user-specified minimum utility threshold (minutil), periodic high-utility pattern mining (PHUPM) aims to identify high-utility patterns that occur periodically in a transaction database. A pattern is deemed periodic if its period aligns with the periodicity constraint set by the user. However, users may not be interested in all periodic high-utility patterns (PHUPs). Moreover, setting minutil in advance is also a challenging issue. To address these issues, our research introduces an algorithm called TPU for extracting the most significant top-k periodic and high-utility patterns that may or may not include negative utility values. This TPU algorithm utilizes positive and negative utility lists (PNUL) and period-estimated utility co-occurrence structure (PEUCS) to store pertinent itemset information. Additionally, it incorporates the periodic real item utility (PIU), periodic co-occurrence utility descending (PCUD), and periodic real utility (PRU) threshold-raising strategies to elevate the thresholds rapidly. By using the proposed threshold-raising strategies, the runtime was reduced by approximately 5% on the datasets used in the experiments. Specifically, the runtime was reduced by up to 50% on the mushroom_negative and kosarak_negative datasets, and by up to 10% on the chess_negative dataset. Memory consumption was reduced by about 2%, with the largest reduction of about 30% observed on the mushroom_negative dataset. Through extensive experiments, we have demonstrated that our algorithm can accurately and effectively extract the top-k periodic high-utility patterns. This paper successfully addresses the top-k mining issue and contributes to data science. Furthermore, the applications of the proposed algorithm in engineering include data mining, expert systems, and web intelligence in various fields, such as smart retail, cyberspace security, and risk prediction. The code and datasets are publicly available at https://github.com/DSI-Lab1/TPU.