research-article

Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

Authors:

Vincent S. Tseng,

Philippe Fournier-Viger,

Philip S. YuAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 27, Issue 3

Pages 726 - 739

https://doi.org/10.1109/TKDE.2014.2345377

Published: 01 March 2015 Publication History

Abstract

Mining high utility itemsets (HUIs) from databases is an important data mining task, which refers to the discovery of itemsets with high utilities (e.g. high profits). However, it may present too many HUIs to users, which also degrades the efficiency of the mining process. To achieve high efficiency for the mining task and provide a concise mining result to users, we propose a novel framework in this paper for mining closed<sup>+</sup> high utility itemsets(CHUIs), which serves as a compact and lossless representation of HUIs. We propose three efficient algorithms named AprioriCH (Apriori-based algorithm for mining High utility Closed<sup>+</sup> itemsets), AprioriHC-D (AprioriHC algorithm with Discarding unpromising and isolated items) and CHUD (Closed<sup>+</sup> High Utility Itemset Discovery) to find this representation. Further, a method called DAHU (Derive All High Utility Itemsets) is proposed to recover all HUIs from the set of CHUIs without accessing the original database. Results on real and synthetic datasets show that the proposed algorithms are very efficient and that our approaches achieve a massive reduction in the number of HUIs. In addition, when all HUIs can be recovered by DAHU, the combination of CHUD and DAHU outperforms the state-of-the-art algorithms for mining HUIs.

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Cited By

Mathur PChand S(2025)Re-induction based mining for high utility item-setsApplied Intelligence10.1007/s10489-024-05855-755:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10489-024-05855-7
Jaysawal BHuang J(2024)SOHUPDS+: An Efficient One-phase Algorithm for Mining High Utility Patterns over a Data StreamACM Transactions on Knowledge Discovery from Data10.1145/370264519:1(1-32)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3702645
Nguyen TTung NNguyen LPham TVo B(2024)MLC-minerExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124383254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124383
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Index Terms

Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets
1. Information systems
  1. Data management systems
  2. Information systems applications
    1. Data mining

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

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 27, Issue 3

March 2015

286 pages

ISSN:1041-4347

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Copyright © 2014.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 March 2015

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Cited By

Mathur PChand S(2025)Re-induction based mining for high utility item-setsApplied Intelligence10.1007/s10489-024-05855-755:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10489-024-05855-7
Jaysawal BHuang J(2024)SOHUPDS+: An Efficient One-phase Algorithm for Mining High Utility Patterns over a Data StreamACM Transactions on Knowledge Discovery from Data10.1145/370264519:1(1-32)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3702645
Nguyen TTung NNguyen LPham TVo B(2024)MLC-minerExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124383254:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124383
Tung NNguyen TNguyen LVo B(2024)An efficient method for mining High-Utility itemsets from unstable negative profit databasesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121489237:PCOnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121489
Mofid ADaneshpour NTorabi Z(2024)MMC: efficient and effective closed high-utility itemset miningThe Journal of Supercomputing10.1007/s11227-024-06224-480:13(18900-18918)Online publication date: 1-Sep-2024
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Duong HPham HTruong TFournier-Viger P(2024)Efficient algorithms to mine concise representations of frequent high utility occupancy patternsApplied Intelligence10.1007/s10489-024-05296-254:5(4012-4042)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10489-024-05296-2
Duong HTruong TLe BFournier-Viger P(2024)CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsetsKnowledge and Information Systems10.1007/s10115-024-02121-766:9(5239-5280)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10115-024-02121-7
Qi YZhang XChen GGan W(2023)Mining periodic trends via closed high utility patternsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120356228:COnline publication date: 15-Oct-2023
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Mondal AMittal RSaurabh SChaudhary PReddy P(2023)An inventory-aware and revenue-based itemset placement framework for retail storesExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119404216:COnline publication date: 15-Apr-2023
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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
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