research-article

A novel approach for mining maximal frequent patterns

Authors:

Zhi-Hong DengAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 73, Issue C

Pages 178 - 186

https://doi.org/10.1016/j.eswa.2016.12.023

Published: 01 May 2017 Publication History

Abstract

An N-list structure is used to compress the dataset for mining Maximal Frequent Patterns.A pruning technique is then proposed and used in INLA-MFP algorithm to improve the runtime and memory usage.Experiments were conducted to show that INLA-MFP outperforms well-known algorithms. Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (FPs) to help intelligent systems operate efficiently. Many approaches have been proposed for mining MFPs, but the complexity of the problem is enormous. Therefore, the run time and memory usage are still large. Recently, the N-list structure has been proposed and verified to be very effective for mining FPs, frequent closed patterns, and top-rank-k FPs. Therefore, this paper uses the N-list structure for mining MFPs. A pruning technique is also proposed to prune branches to reduce the search space. This technique is applied to an algorithm called INLA-MFP (improved N-list-based algorithm for mining maximal frequent patterns) for mining MFPs. Experiments were conducted to evaluate the effectiveness of the proposed algorithm. The experimental results show that INLA-MFP outperforms two state-of-the-art algorithms for mining MFPs.

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Cited By

Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3658450
Rodríguez-González AAranda RÁlvarez-Carmona MDíaz-Pacheco ARosas R(2024)X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/364382018:5(1-26)Online publication date: 30-Jan-2024
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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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A novel approach for mining maximal frequent patterns
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 73, Issue C

May 2017

98 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2017

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Cited By

Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3658450
Rodríguez-González AAranda RÁlvarez-Carmona MDíaz-Pacheco ARosas R(2024)X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/364382018:5(1-26)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.1145/3643820
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
https://dl.acm.org/doi/10.1007/s11227-024-06224-4
Nguyen T(2024)nMITP-Miner: An Efficient Method for Mining Frequent Maximal Inter-transaction PatternsComputational Collective Intelligence10.1007/978-3-031-70816-9_27(346-358)Online publication date: 9-Sep-2024
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Le NVo BYun ULe B(2023)A novel approach to discover frequent weighted subgraphs using the average measureApplied Intelligence10.1007/s10489-023-04501-y53:16(19491-19504)Online publication date: 8-Mar-2023
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Wu YWang XLi YGuo LLi ZZhang JWu X(2022)OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern MiningACM Transactions on Management Information Systems10.1145/347624713:3(1-23)Online publication date: 4-Feb-2022
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Agor JLi RÖzaltın O(2022)Septic shock prediction and knowledge discovery through temporal pattern miningArtificial Intelligence in Medicine10.1016/j.artmed.2022.102406132:COnline publication date: 1-Oct-2022
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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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Darrab SBroneske DSaake G(2021)MaxRI: A method for discovering maximal rare itemsets2021 4th International Conference on Data Science and Information Technology10.1145/3478905.3478972(334-341)Online publication date: 23-Jul-2021
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Ledmi MZidat SHamdi-Cherif A(2021)GrAFCI+ A fast generator-based algorithm for mining frequent closed itemsetsKnowledge and Information Systems10.1007/s10115-021-01575-363:7(1873-1908)Online publication date: 1-Jul-2021
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