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2024 Volume 39
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REVIEW   Open Access    

Top-k high utility itemset mining: current status and future directions

  • 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: High utility itemsets mining (HUIM) is an important sub-field of frequent itemset mining (FIM). Recently, HUIM has received much attention in the field of data mining. High utility itemsets (HUIs) have proven to be quite useful in marketing, retail marketing, cross-marketing, and e-commerce. Traditional HUIM approaches suffer from a drawback as they need a user-defined minimum utility ($ min\_util $) threshold. It is not easy for the users to set the appropriate $ min\_util $ threshold to find actionable HUIs. To target this drawback, top-k HUIM has been introduced. top-k HUIM is more suitable for supermarket managers and retailers to prepare appropriate strategies to generate higher profit. In this paper, we provide an in-depth survey of the current status of top-k HUIM approaches. The paper presents the task of top-k HUIM and its relevant definitions. It reviews the top-k HUIM approaches and presents their advantages and disadvantages. The paper also discusses the important strategies of the top-k HUIM, their variations, and research opportunities. The paper provides a detailed summary, analysis, and future directions of the top-k HUIM field.
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  • Cite this article

    Rajiv Kumar, Kuldeep Singh. 2024. Top-k high utility itemset mining: current status and future directions. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000055
    Rajiv Kumar, Kuldeep Singh. 2024. Top-k high utility itemset mining: current status and future directions. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000055
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Top-k high utility itemset mining: current status and future directions

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

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

  • Received: 07 May 2023
  • Revised: 26 March 2024
  • Accepted: 10 June 2024
  • Published online: 06 November 2024
The Knowledge Engineering Review  39,  2024, 39(1)  |  Cite this article

Abstract: Abstract: High utility itemsets mining (HUIM) is an important sub-field of frequent itemset mining (FIM). Recently, HUIM has received much attention in the field of data mining. High utility itemsets (HUIs) have proven to be quite useful in marketing, retail marketing, cross-marketing, and e-commerce. Traditional HUIM approaches suffer from a drawback as they need a user-defined minimum utility ($ min\_util $) threshold. It is not easy for the users to set the appropriate $ min\_util $ threshold to find actionable HUIs. To target this drawback, top-k HUIM has been introduced. top-k HUIM is more suitable for supermarket managers and retailers to prepare appropriate strategies to generate higher profit. In this paper, we provide an in-depth survey of the current status of top-k HUIM approaches. The paper presents the task of top-k HUIM and its relevant definitions. It reviews the top-k HUIM approaches and presents their advantages and disadvantages. The paper also discusses the important strategies of the top-k HUIM, their variations, and research opportunities. The paper provides a detailed summary, analysis, and future directions of the top-k HUIM field.

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    • If an item is non-frequent, then all of its super-sets are non-frequent.

    • An itemset is HTWUI (High Transaction-Weighted Utility Itemset) if its TWU is no less than the minimum-utility threshold specified by the user.

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    • This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
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    Rajiv Kumar, Kuldeep Singh. 2024. Top-k high utility itemset mining: current status and future directions. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000055
    Rajiv Kumar, Kuldeep Singh. 2024. Top-k high utility itemset mining: current status and future directions. The Knowledge Engineering Review 39(1), doi: 10.1017/S0269888924000055
    shu

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