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2025 Volume 40
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Mining of high utility itemsets from incremental datasets: a survey

  • School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

  • Department of Computer Science, University of Delhi, Delhi 110–007, India

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  • Abstract: Traditional frequent itemset mining (FIM) is constrained by several limitations, mainly due to its failure to account for item quantity and significance, including factors such as price and profit. To address these limitations, high utility itemset mining (HUIM) is presented. Traditional HUIM algorithms are designed to operate solely on static transactional datasets. Nevertheless, in practical applications, datasets tend to be dynamic, with examples like market basket analysis and business decision-making involving regular updates to the data. Dynamic datasets are updated incrementally with the frequent addition of new data. Incremental HUIM (iHUIM) approaches mine the high utility itemsets (HUIs) from incremental datasets without scanning the whole dataset. In contrast, traditional HUIM approaches require a full dataset scan each time the dataset is updated. Consequently, iHUIM approaches effectively reduce the computational cost of identifying HUIs whenever a new record is added. This survey provides a novel taxonomy that includes two-based, pattern-growth-based, projection-based, utility-list-based, and pre-large-based algorithms. The paper delivers an in-depth analysis, covering the features and characteristics of the existing state-of-the-art algorithms. Additionally, it supplies a detailed comparative overview, advantages, disadvantages, and future research directions of these algorithms. The survey provides both a categorized analysis and a comprehensive, consolidated summary and analysis of all current state-of-the-art iHUIM algorithms. It offers a more in-depth comparative analysis than the currently available state-of-the-art surveys. Additionally, the survey highlights several research opportunities and future directions for iHUIM.
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  • Cite this article

    Rajiv Kumar, Kuldeep Singh. 2025. Mining of high utility itemsets from incremental datasets: a survey. The Knowledge Engineering Review 40(1), doi: 10.1017/S0269888925000013
    Rajiv Kumar, Kuldeep Singh. 2025. Mining of high utility itemsets from incremental datasets: a survey. The Knowledge Engineering Review 40(1), doi: 10.1017/S0269888925000013
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Mining of high utility itemsets from incremental datasets: a survey

  • School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

  • Department of Computer Science, University of Delhi, Delhi 110–007, India

  • Received: 01 May 2024
  • Revised: 11 March 2025
  • Accepted: 13 March 2025
  • Published online: 28 April 2025
The Knowledge Engineering Review  40,  2025, 40(1)  |  Cite this article

Abstract: Abstract: Traditional frequent itemset mining (FIM) is constrained by several limitations, mainly due to its failure to account for item quantity and significance, including factors such as price and profit. To address these limitations, high utility itemset mining (HUIM) is presented. Traditional HUIM algorithms are designed to operate solely on static transactional datasets. Nevertheless, in practical applications, datasets tend to be dynamic, with examples like market basket analysis and business decision-making involving regular updates to the data. Dynamic datasets are updated incrementally with the frequent addition of new data. Incremental HUIM (iHUIM) approaches mine the high utility itemsets (HUIs) from incremental datasets without scanning the whole dataset. In contrast, traditional HUIM approaches require a full dataset scan each time the dataset is updated. Consequently, iHUIM approaches effectively reduce the computational cost of identifying HUIs whenever a new record is added. This survey provides a novel taxonomy that includes two-based, pattern-growth-based, projection-based, utility-list-based, and pre-large-based algorithms. The paper delivers an in-depth analysis, covering the features and characteristics of the existing state-of-the-art algorithms. Additionally, it supplies a detailed comparative overview, advantages, disadvantages, and future research directions of these algorithms. The survey provides both a categorized analysis and a comprehensive, consolidated summary and analysis of all current state-of-the-art iHUIM algorithms. It offers a more in-depth comparative analysis than the currently available state-of-the-art surveys. Additionally, the survey highlights several research opportunities and future directions for iHUIM.

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    Rajiv Kumar, Kuldeep Singh. 2025. Mining of high utility itemsets from incremental datasets: a survey. The Knowledge Engineering Review 40(1), doi: 10.1017/S0269888925000013
    Rajiv Kumar, Kuldeep Singh. 2025. Mining of high utility itemsets from incremental datasets: a survey. The Knowledge Engineering Review 40(1), doi: 10.1017/S0269888925000013
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