research-article

Correlated utility-based pattern mining

Authors:

Jerry Chun-Wei Lin,

Han-Chieh Chao,

Philip S. YuAuthors Info & Claims

Volume 504, Issue C

Pages 470 - 486

https://doi.org/10.1016/j.ins.2019.07.005

Published: 01 December 2019 Publication History

Abstract

Recently, a new research field called utility-oriented mining has attracted great attention. However, previous studies have shown a limitation in that they rarely consider the inherent correlation of items among patterns. For example, considering the purchase behaviors of consumers, a high-utility group of products (w.r.t. multi-products) may contain several very high-utility products with some low-utility products. However, it is considered to be a valuable pattern even if this behavior/pattern may not be highly correlated, or even if it happens by chance. In light of these challenges, we propose an efficient utility-mining approach, called non-redundant Co rrelated high- U tility P attern M iner (CoUPM) by considering the positive correlation and profitable value. The derived patterns with high utility and strong positive correlation can lead to more insightful availability than those patterns that only have high profitable values. The utility-list structure is revised and applied to store the necessary information of both correlation and utility. Several pruning strategies are further developed to improve the efficiency for discovering the desired patterns. Experimental results showed that the non-redundant correlated high-utility patterns have more effectiveness than some other kinds of interesting patterns. Moreover, the efficiency of the proposed CoUPM algorithm significantly outperformed the state-of-the-art algorithm.

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Cited By

Huang SGan WMiao JHan XFournier-Viger P(2024)Targeted mining of top-k high utility itemsetsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107047126:PDOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107047
Zhang CYang YDu ZGan WYu P(2023)HUSP-SP: Faster Utility Mining on Sequence DataACM Transactions on Knowledge Discovery from Data10.1145/359793518:1(1-21)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3597935
Zhang XChen GSong LGan WSong Y(2023)HEPMKnowledge-Based Systems10.1016/j.knosys.2023.111068281:COnline publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.111068
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Index Terms

Correlated utility-based pattern mining
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 504, Issue C

Dec 2019

602 pages

ISSN:0020-0255

Issue’s Table of Contents

Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 December 2019

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Cited By

Huang SGan WMiao JHan XFournier-Viger P(2024)Targeted mining of top-k high utility itemsetsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107047126:PDOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107047
Zhang CYang YDu ZGan WYu P(2023)HUSP-SP: Faster Utility Mining on Sequence DataACM Transactions on Knowledge Discovery from Data10.1145/359793518:1(1-21)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3597935
Zhang XChen GSong LGan WSong Y(2023)HEPMKnowledge-Based Systems10.1016/j.knosys.2023.111068281:COnline publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.111068
Han MLi MChen ZWu HZhang X(2023)High utility pattern mining algorithm over data streams using ext-list.Applied Intelligence10.1007/s10489-023-04925-653:22(27072-27095)Online publication date: 31-Aug-2023
https://dl.acm.org/doi/10.1007/s10489-023-04925-6
Gui YGan WChen YWu Y(2022)Mining with Rarity for Web IntelligenceCompanion Proceedings of the Web Conference 202210.1145/3487553.3524708(973-981)Online publication date: 25-Apr-2022
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Somyanonthanakul RTheeramunkong T(2022)Scenario-based analysis for discovering relations among interestingness measuresInformation Sciences: an International Journal10.1016/j.ins.2021.12.121590:C(346-385)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.ins.2021.12.121
Ma L(2022)Novel next-group recommendation approach based on sequential market basket informationElectronic Commerce Research10.1007/s10660-022-09543-x23:4(2399-2418)Online publication date: 19-Mar-2022
https://dl.acm.org/doi/10.1007/s10660-022-09543-x
Nguyen LNguyen LVo BZelinka ILin JYun UNguyen H(2022)An efficient and scalable approach for mining subgraphs in a single large graphApplied Intelligence10.1007/s10489-022-03164-552:15(17881-17895)Online publication date: 1-Dec-2022
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Truong TDuong HLe BFournier-Viger PYun U(2022)Frequent high minimum average utility sequence mining with constraints in dynamic databases using efficient pruning strategiesApplied Intelligence10.1007/s10489-021-02520-152:6(6106-6128)Online publication date: 1-Apr-2022
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Bui QVo BDo HHung NSnasel V(2020)F-MapperKnowledge-Based Systems10.1016/j.knosys.2019.105107189:COnline publication date: 15-Feb-2020
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