research-article

High-Utility Itemset Mining with Effective Pruning Strategies

Authors:

Jimmy Ming-Tai Wu,

Jerry Chun-Wei Lin,

Ashish TamrakarAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 6

Article No.: 58, Pages 1 - 22

https://doi.org/10.1145/3363571

Published: 11 November 2019 Publication History

Abstract

High-utility itemset mining is a popular data mining problem that considers utility factors, such as quantity and unit profit of items besides frequency measure from the transactional database. It helps to find the most valuable and profitable products/items that are difficult to track by using only the frequent itemsets. An item might have a high-profit value which is rare in the transactional database and has a tremendous importance. While there are many existing algorithms to find high-utility itemsets (HUIs) that generate comparatively large candidate sets, our main focus is on significantly reducing the computation time with the introduction of new pruning strategies. The designed pruning strategies help to reduce the visitation of unnecessary nodes in the search space, which reduces the time required by the algorithm. In this article, two new stricter upper bounds are designed to reduce the computation time by refraining from visiting unnecessary nodes of an itemset. Thus, the search space of the potential HUIs can be greatly reduced, and the mining procedure of the execution time can be improved. The proposed strategies can also significantly minimize the transaction database generated on each node. Experimental results showed that the designed algorithm with two pruning strategies outperform the state-of-the-art algorithms for mining the required HUIs in terms of runtime and number of revised candidates. The memory usage of the designed algorithm also outperforms the state-of-the-art approach. Moreover, a multi-thread concept is also discussed to further handle the problem of big datasets.

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Cited By

Chen JLiu AZhang HYang SZheng HZhou NLi P(2024)Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systemsScientific Reports10.1038/s41598-023-50375-y14:1Online publication date: 10-Jan-2024
https://doi.org/10.1038/s41598-023-50375-y
Song WSun ZFournier-Viger PWu Y(2024)MRI-CE: Minimal rare itemset discovery using the cross-entropy methodInformation Sciences10.1016/j.ins.2024.120392(120392)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120392
Yacoubi SManita GChhabra AKorbaa O(2024)A Metaheuristic Perspective on Extracting Numeric Association Rules: Current Works, Applications, and RecommendationsArchives of Computational Methods in Engineering10.1007/s11831-024-10109-3Online publication date: 29-Mar-2024
https://doi.org/10.1007/s11831-024-10109-3
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Index Terms

High-Utility Itemset Mining with Effective Pruning Strategies
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Association rules
    2. Decision support systems
      1. Data analytics

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 6

December 2019

282 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3366748

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

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Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 11 November 2019

Accepted: 01 August 2019

Revised: 01 February 2019

Received: 01 February 2017

Published in TKDD Volume 13, Issue 6

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Cited By

Chen JLiu AZhang HYang SZheng HZhou NLi P(2024)Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systemsScientific Reports10.1038/s41598-023-50375-y14:1Online publication date: 10-Jan-2024
https://doi.org/10.1038/s41598-023-50375-y
Song WSun ZFournier-Viger PWu Y(2024)MRI-CE: Minimal rare itemset discovery using the cross-entropy methodInformation Sciences10.1016/j.ins.2024.120392(120392)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120392
Yacoubi SManita GChhabra AKorbaa O(2024)A Metaheuristic Perspective on Extracting Numeric Association Rules: Current Works, Applications, and RecommendationsArchives of Computational Methods in Engineering10.1007/s11831-024-10109-3Online publication date: 29-Mar-2024
https://doi.org/10.1007/s11831-024-10109-3
Wu JLi RWu MLin J(2023)Mining skyline frequent-utility patterns from big data environment based on MapReduce frameworkIntelligent Data Analysis10.3233/IDA-22075627:5(1359-1377)Online publication date: 6-Oct-2023
https://doi.org/10.3233/IDA-220756
Wu JLi RHsu PWu M(2023)The effective skyline quantify-utility patterns mining algorithm with pruning strategiesComputer Science and Information Systems10.2298/CSIS220615040W20:3(1085-1108)Online publication date: 2023
https://doi.org/10.2298/CSIS220615040W
Wang LTran VDo T(2023)A Clique-Querying Mining Framework for Discovering High Utility Co-Location Patterns without Generating CandidatesACM Transactions on Knowledge Discovery from Data10.1145/361737818:1(1-42)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617378
Qu JFournier-Viger PLiu MHang BHu C(2023)Mining High Utility Itemsets Using Prefix Trees and Utility VectorsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325612635:10(10224-10236)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3256126
Kimura GHayamizu YKiran RKitsuregawa MGoda K(2023)Efficient Parallel Mining of High-utility Itemsets on Multicore Processors2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00388(638-652)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00388
Kimura GHayamizu YKiran RKitsuregawa MGoda K(2023)Efficient Parallel Mining of High-utility Itemsets on Multicore Processors2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00384(3563-3577)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00384
Vu VLam MDuong TManh LNguyen TNguyen LYun USnasel VVo B(2023)FTKHUIM: A Fast and Efficient Method for Mining Top-K High-Utility ItemsetsIEEE Access10.1109/ACCESS.2023.331498411(104789-104805)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3314984
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