research-article

Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields

Authors:

Eunchul YoonAuthors Info & Claims

Volume 494, Issue C

Pages 37 - 59

https://doi.org/10.1016/j.ins.2019.04.050

Published: 01 August 2019 Publication History

Abstract

As one of the various data mining areas, erasable pattern mining is a method for solving financial problems that can be caused in various industrial fields. Since the concept of erasable pattern mining was proposed, various relevant techniques have been developed. Dealing with dynamic stream data is an important issue in different data mining areas including erasable pattern mining. In order to deal with partial data with the latest information, a sliding window-based approach was proposed. However, the method still has performance limits in many cases because of its inefficient data structures and mining techniques. Motivated by this problem, we propose a new efficient erasable pattern mining algorithm based on the sliding window that can guarantee robustness and reliability and deal with the latest stream data more effectively. In addition, the newly proposed list data structure and mining techniques allow the proposed method to extract erasable patterns in a more efficient way. Results of empirical experiments using well-known benchmark datasets guarantee that the proposed algorithm outperforms previous state-of-the-art approaches in various aspects.

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Alsaeedi HAlhegami A(2022)An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth AlgorithmComplexity10.1155/2022/19425172022Online publication date: 1-Jan-2022
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Lee CBaek YLin JTruong TYun U(2022)Advanced uncertainty based approach for discovering erasable product patternsKnowledge-Based Systems10.1016/j.knosys.2022.108134241:COnline publication date: 6-Apr-2022
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Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 494, Issue C

Aug 2019

328 pages

ISSN:0020-0255

Issue’s Table of Contents

Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 August 2019

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Cited By

Kim HKim HKim SKim HCho MVo BChun-Wei Lin JYun U(2023)An advanced approach for incremental flexible periodic pattern mining on time-series dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120697230:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120697
Alsaeedi HAlhegami A(2022)An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth AlgorithmComplexity10.1155/2022/19425172022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1942517
Lee CBaek YLin JTruong TYun U(2022)Advanced uncertainty based approach for discovering erasable product patternsKnowledge-Based Systems10.1016/j.knosys.2022.108134241:COnline publication date: 6-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108134
Lee CBaek YRyu TKim HKim HChun-Wei Lin JVo BYun U(2022)An efficient approach for mining maximized erasable utility patternsInformation Sciences: an International Journal10.1016/j.ins.2022.07.037609:C(1288-1308)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.07.037
Kim HRyu TLee CKim HYoon EVo BChun-Wei Lin JYun U(2022)EHMINExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118214209:COnline publication date: 15-Dec-2022
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Zhao FLi XGao YLi YFeng ZZhang C(2022)Multi-layer features ablation of BERT model and its application in stock trend predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117958207:COnline publication date: 30-Nov-2022
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Huo XHao KChen LTang XWang TCai X(2022)A dynamic soft sensor of industrial fuzzy time series with propositional linear temporal logicExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117176201:COnline publication date: 1-Sep-2022
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Hu KQiu LZhang SWang ZFang N(2022)An incremental rare association rule mining approach with a life cycle tree structure considering time-sensitive dataApplied Intelligence10.1007/s10489-022-03978-353:9(10800-10824)Online publication date: 25-Aug-2022
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Wang YWu YLi YYao FFournier-Viger PWu X(2022)Self-adaptive nonoverlapping sequential pattern miningApplied Intelligence10.1007/s10489-021-02763-y52:6(6646-6661)Online publication date: 1-Apr-2022
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Wu YGeng MLi YGuo LLi ZFournier-Viger PZhu XWu X(2021)HANP-MinerKnowledge-Based Systems10.1016/j.knosys.2021.107361229:COnline publication date: 11-Oct-2021
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