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High utility itemsets mining from transactional databases: a survey

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Abstract

Mining high utility itemsets are the basic task in the area of frequent itemset mining (FIM) that has various applications in diverse domains, including market basket analysis, web mining, cross-marketing, and e-commerce. In recent years, many efficient high utility itemsets mining (HUIM) algorithms are proposed to discover the high utility itemsets (HUIs). This survey presents a comprehensive summary of the current state-of-the-art HUIM approaches for transactional databases. This paper categorises the state-of-the-art approaches as level-wise, tree-based, utility-list-based, projection-based and miscellaneous. It provides the pros and cons of each category of mining approaches in detail. A taxonomy of the HUIM for transactional databases is presented. The survey also summarises and discusses approaches for other types of databases, including on-shelf, dynamic and uncertain. The paper explores the applications of HUIM in diverse domains and discusses the challenges and limitations of the approach. It presents an overview of 16 real-world which are utilized by various state-of-the-art HUIM approaches for transactional databases. Overall, this survey provides a valuable resource for researchers in the field of HUIM and offers insights into future directions for research and development in this area.

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Our work does not have any data to explore. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. A k-itemset is HUIs only if all of its sub-itemsets are HUIs.

  2. If a set fails to pass a test, all the supersets will also fail the same test as well.

  3. CHUI is a group of itemsets that have no supersets with the same support count in the database.

  4. All the prefix extensions of an itemset with relevant items, are high utility itemsets without creating the rest of its subtree.

  5. An itemset is high utility itemset and all its proper subsets are not, without creating the rest of its subtrees.

  6. The coverage is used to prune low utility itemsets and to quickly calculates the closure of itemsets.

  7. Lattice is an effective approach for data analysis and knowledge discovery to mine the association rules.

  8. The temporal HUIs are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream.

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Authors and Affiliations

  1. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India

    Rajiv Kumar

  2. Department of Computer Science, University of Delhi, Delhi, India

    Kuldeep Singh

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Contributions

Author Contributions K.S. conceived analysis idea and presented taxonomy. R.K. developed the theory and analysis of the state-of-the-art approaches. R.K. provided the data for Tables 7 to 17. K.S. implemented the example and tables related to the running example. R.K. performed the analysis part at Section 3. K.S. collected all the required data. K.S. Reviewed and Edited the manuscript. Both the authors analyzed and contributed to the final manuscript.

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Correspondence to Kuldeep Singh.

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Kumar, R., Singh, K. High utility itemsets mining from transactional databases: a survey. Appl Intell 53, 27655–27703 (2023). https://doi.org/10.1007/s10489-023-04853-5

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