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High-utility itemset mining (HUIM) major has a lot of research in recent years. Almost all published algorithms focus on processing static databases, which do not utilize previously mined information to mine incremental databases. To solve this problem, some incremental HUIM was published and showed the possibility of development. In this study, a new algorithm named iHUIM based on the Efficient Incremental High Utility Itemset (EIHI) algorithm was proposed. Unlike EIHI, which requires twice database scans, the iHUIM just scans the database only once. Additionally, using compact utility list and some pruning strategies, iHUIM shows outperformance EIHI regarding the length of execution time and has a slight improvement in memory consumption.

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Effective High Utility Itemsets Mining Algorithm for Incremental Databases

  • 会议论文

  • 发表日期：

    2025年

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  • Do Thanh Cong,
  • Do Mai Phuong,
  • Pham Duc Duong,
  • Phan Duy Hung

摘要

High-utility itemset mining (HUIM) major has a lot of research in recent years. Almost all published algorithms focus on processing static databases, which do not utilize previously mined information to mine incremental databases. To solve this problem, some incremental HUIM was published and showed the possibility of development. In this study, a new algorithm named iHUIM based on the Efficient Incremental High Utility Itemset (EIHI) algorithm was proposed. Unlike EIHI, which requires twice database scans, the iHUIM just scans the database only once. Additionally, using compact utility list and some pruning strategies, iHUIM shows outperformance EIHI regarding the length of execution time and has a slight improvement in memory consumption.