Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM met...

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Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 34; číslo 8; s. 1
Hlavní autoři:	Fang, Wei, Zhang, Qiang, Sun, Jun, Wu, Xiao-Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Convergence Data mining Datasets Evolutionary algorithms Evolutionary computation Genetic algorithms Itemsets multi-objective evolutionary algorithm occupancy Optimization Pattern analysis Pattern mining Sociology Solution space Statistics support utility
ISSN:	1041-4347, 1558-2191
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Abstract	Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.
AbstractList	Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.
Author	Sun, Jun Wu, Xiao-Jun Fang, Wei Zhang, Qiang
Author_xml	– sequence: 1 givenname: Wei surname: Fang fullname: Fang, Wei email: fangwei@jiangnan.edu.cn organization: Department of Computer Science and Technology, Jiangnan University, 66374 Wuxi, Jiangsu, China, (e-mail: fangwei@jiangnan.edu.cn) – sequence: 2 givenname: Qiang surname: Zhang fullname: Zhang, Qiang email: 1227553789@qq.com organization: Department of Computer Science and Technology, Jiangnan University, 66374 Wuxi, Jiangsu, China, (e-mail: 1227553789@qq.com) – sequence: 3 givenname: Jun surname: Sun fullname: Sun, Jun email: sunjun_wx@hotmail.com organization: Department of Computer Science and Technology, Jiangnan University, 66374 Wuxi, Jiangsu, China, (e-mail: sunjun_wx@hotmail.com) – sequence: 4 givenname: Xiao-Jun surname: Wu fullname: Wu, Xiao-Jun email: wu_xiaojun@jiangnan.edu.cn organization: Department of Computer Science and Technology, Jiangnan University, 66374 Wuxi, Jiangsu, China, (e-mail: wu_xiaojun@jiangnan.edu.cn)
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References	Han (ref2) 2011 ref13 ref57 ref12 ref56 ref15 Liu (ref48) ref59 ref14 ref58 ref53 Agrawal (ref8); 1215 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 Savasere (ref42) 1995 ref18 Coello (ref69) 2007; 5 ref51 ref50 ref46 ref45 ref47 ref86 Venkatadri (ref32) 2010; 1 ref41 ref85 ref44 ref87 ref49 ref7 ref9 ref4 ref3 ref6 ref5 ref82 ref81 ref40 ref84 ref83 ref80 ref35 ref79 ref34 ref78 ref37 ref36 ref31 ref75 ref30 ref74 ref33 ref77 ref76 ref1 ref39 ref38 ref71 ref70 ref73 ref72 Kalyanmoy (ref27) 2001 ref24 ref68 ref23 ref67 ref26 ref25 ref20 Toivonen (ref43); 96 ref64 ref63 ref22 ref66 ref21 ref65 ref28 ref29 ref60 ref62 ref61
References_xml	– ident: ref4 doi: 10.1109/TETCI.2020.3000224 – ident: ref71 doi: 10.1016/j.swevo.2011.03.001 – ident: ref81 doi: 10.1109/TEVC.2018.2855411 – ident: ref61 doi: 10.1016/j.asoc.2009.11.023 – volume: 96 start-page: 134 volume-title: Proc. 22th Int. Conf. Very Large Data Bases ident: ref43 article-title: Sampling large databases for association rules – ident: ref57 doi: 10.1016/j.eswa.2014.11.001 – ident: ref83 doi: 10.1109/ICSMC.2009.5346628 – ident: ref31 doi: 10.1109/TEVC.2013.2290086 – ident: ref52 doi: 10.1109/TKDE.2009.46 – ident: ref73 doi: 10.1109/CEC.2013.6557902 – ident: ref82 doi: 10.1109/TMAG.2017.2661987 – ident: ref65 doi: 10.1007/s00500-016-2106-1 – ident: ref39 doi: 10.1109/ICDM.2011.118 – ident: ref74 doi: 10.1109/CEC.2014.6900618 – ident: ref47 doi: 10.1109/TKDE.2005.166 – ident: ref25 doi: 10.1109/MCI.2017.2708578 – ident: ref7 doi: 10.1145/2396761.2396775 – ident: ref44 doi: 10.1145/253260.253325 – ident: ref24 doi: 10.1109/TITS.2020.3012387 – ident: ref41 doi: 10.1145/568271.223813 – ident: ref55 doi: 10.1007/978-3-319-08326-1_9 – ident: ref30 doi: 10.1109/MCI.2018.2806997 – start-page: 432 year: 1995 ident: ref42 article-title: An efficient algorithm for mining association rules in large databases – ident: ref34 doi: 10.1145/2330163.2330285 – ident: ref58 doi: 10.1016/j.ins.2020.02.073 – volume-title: Data Mining: Concepts and Techniques year: 2011 ident: ref2 – ident: ref11 doi: 10.1137/1.9781611972726.27 – ident: ref87 doi: 10.1109/MCI.2017.2742868 – ident: ref72 doi: 10.1109/4235.996017 – ident: ref12 doi: 10.1109/TKDE.2015.2399310 – ident: ref86 doi: 10.1109/TEVC.2020.3013290 – ident: ref75 doi: 10.1016/j.knosys.2006.08.005 – ident: ref77 doi: 10.1016/j.knosys.2008.03.011 – ident: ref63 doi: 10.1016/j.engappai.2016.07.006 – ident: ref3 doi: 10.1007/978-3-030-04921-8 – start-page: 1 volume-title: Proc. ICDM Workshop Frequent Itemset Mining Implementations ident: ref48 article-title: AFOPT: An efficient implementation of pattern growth approach – ident: ref50 doi: 10.1109/69.846291 – volume: 1 start-page: 443 issue: 5 year: 2010 ident: ref32 article-title: A multiobjective genetic algorithm for feature selection in data mining publication-title: Int. J. Comput. Sci. Inf. Technol. – ident: ref62 doi: 10.1080/08839514.2014.891839 – ident: ref15 doi: 10.1007/11430919_79 – ident: ref85 doi: 10.1109/TEVC.2005.851275 – ident: ref29 doi: 10.1109/MCI.2014.2369894 – ident: ref22 doi: 10.1109/TCYB.2019.2896267 – ident: ref79 doi: 10.1007/978-3-319-46131-1_8 – volume-title: Multi Objective Optimization Using Evolutionary Algorithms year: 2001 ident: ref27 – ident: ref18 doi: 10.1145/2396761.2396773 – ident: ref1 doi: 10.1007/s10618-006-0059-1 – ident: ref67 doi: 10.1109/ACCESS.2019.2958150 – ident: ref21 doi: 10.1109/TKDE.2015.2458860 – ident: ref54 doi: 10.1007/978-3-319-27060-9_44 – volume: 5 volume-title: Evolutionary Algorithms for Solving Multi-Objective Problems year: 2007 ident: ref69 – ident: ref70 doi: 10.1007/s10589-014-9644-1 – ident: ref13 doi: 10.1137/1.9781611972740.51 – ident: ref60 doi: 10.1016/j.ins.2016.03.039 – ident: ref33 doi: 10.1016/j.ins.2013.01.028 – volume: 1215 start-page: 487 volume-title: Proc. 20th Int. Conf. Very Large Data Bases ident: ref8 article-title: Fast algorithms for mining association rules – ident: ref51 doi: 10.1016/j.datak.2007.06.009 – ident: ref16 doi: 10.1145/1835804.1835839 – ident: ref78 doi: 10.4018/ijdwm.2014010101 – ident: ref14 doi: 10.1109/TKDE.2019.2942594 – ident: ref59 doi: 10.1145/508791.508905 – ident: ref66 doi: 10.1109/ACCESS.2018.2819162 – ident: ref84 doi: 10.1109/4235.797969 – ident: ref19 doi: 10.1109/BigData.2018.8622405 – ident: ref36 doi: 10.1145/2742642 – ident: ref37 doi: 10.1140/epjds/s13688-018-0133-0 – ident: ref20 doi: 10.1002/widm.1242 – ident: ref64 doi: 10.1109/ICSMC.1997.637339 – ident: ref45 doi: 10.1006/jpdc.2000.1693 – ident: ref23 doi: 10.1109/TCYB.2020.2970176 – ident: ref26 doi: 10.1016/j.asoc.2017.09.033 – ident: ref28 doi: 10.1109/MCI.2006.1597059 – ident: ref40 doi: 10.1007/978-3-319-31753-3_29 – ident: ref46 doi: 10.1145/1454008.1454027 – ident: ref5 doi: 10.1109/JSEN.2020.2991045 – ident: ref10 doi: 10.1109/ICDM.2001.989550 – ident: ref49 doi: 10.1109/CIS.2007.177 – ident: ref53 doi: 10.1016/j.eswa.2017.08.028 – ident: ref76 doi: 10.1109/TKDE.2005.183 – ident: ref35 doi: 10.1007/s00500-019-03829-3 – ident: ref9 doi: 10.1145/335191.335372 – ident: ref56 doi: 10.1109/ICDM.2012.20 – ident: ref17 doi: 10.1109/TKDE.2012.59 – ident: ref38 doi: 10.1145/2753765 – ident: ref80 doi: 10.1109/TEVC.2018.2875430 – ident: ref6 doi: 10.1145/170036.170072 – ident: ref68 doi: 10.1109/TEVC.2019.2918140
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SubjectTerms	Convergence Data mining Datasets Evolutionary algorithms Evolutionary computation Genetic algorithms Itemsets multi-objective evolutionary algorithm occupancy Optimization Pattern analysis Pattern mining Sociology Solution space Statistics support utility
Title	Mining High Quality Patterns Using Multi-Objective Evolutionary Algorithm
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