Abstract
Traditional frequent itemsets mining (FIM) suffers from the vast memory cost, small processing speed and insufficient disk space requirements. FIM assumes only binary frequency value for items in the dataset and assumes equal importance value for items. In order to target all these limitations of FIM, high-utility itemsets (HUIs) mining has been presented. HUIs mining is more complicated and difficult than FIM. HUIs mining algorithms spend more execution time because of large search space. Therefore, soft computing techniques-based HUIs mining has been proposed. Soft computing techniques provide a systematic process to discover the optimum solutions by using the concept of natural evolution. This article explores the usage of soft computing techniques in HUIs mining. The article presents a taxonomy of soft computing techniques-based HUIs mining including evolutionary computation and fuzzy logic-based approaches. This article enhances understanding of HUIs mining problems, the current status of provided solutions. The paper provides a comparison analysis of key techniques and discusses theoretical aspects of the various nature-inspired and fuzzy logic-based techniques along with their pros and cons. In addition, an overview of strategies and approaches is also presented. Finally, the article mentions key research opportunities and future directions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agrawal S, Silakari S (2014) Frpso: Fletcher—reeves based particle swarm optimization for multimodal function optimization. Soft Comput 18(11):2227–2243. https://doi.org/10.1007/s00500-013-1196-2
Agrawal R, Srikant R (2000) Privacy-preserving data mining. SIGMOD Rec. 29(2):439–450. https://doi.org/10.1145/335191.335438
Agrawal R, Imielinski T, Swami A (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925. https://doi.org/10.1109/69.250074
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, VLDB ’94, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, p 487–499
Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the eleventh international conference on Data engineering, pp 3–14. https://doi.org/10.1109/ICDE.1995.380415
Ahmed CF, Tanbeer SK, Jeong B-S, Lee Y-K (2009) Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans Knowl Data Eng 21(12):1708–1721. https://doi.org/10.1109/TKDE.2009.46
Ahmed U, Lin JC-W, Srivastava G, Yasin R, Djenouri Y (2021) An evolutionary model to mine high expected utility patterns from uncertain databases. IEEE Trans Emerg Topic Comput Intell 5(1):19–28. https://doi.org/10.1109/TETCI.2020.3000224
Alcalá-Fdez J, Alcalá R, Gacto MJ, Herrera F (2009) Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst 160(7):905–921. https://doi.org/10.1016/j.fss.2008.05.012
Alhajj R, Kaya M (2007) Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining. J Intell Inf Syst 31(3):243–264. https://doi.org/10.1007/s10844-007-0044-1
Almaden I (1994) Quest synthetic data generation code
Amiri A (2007) Dare to share: protecting sensitive knowledge with data sanitization, decision Support Systems 43(1), 181–191, mobile commerce: strategies, technologies, and applications. https://doi.org/10.1016/j.dss.2006.08.007. https://www.sciencedirect.com/science/article/pii/S0167923606001230
Arunkumar MS, Suresh P, Gunavathi C (2020) High utility infrequent itemset mining using a customized ant colony algorithm. Int. J. Parallel Program. 48(5):833–849. https://doi.org/10.1007/s10766-018-0621-7
Asuncion A, Newman D (2007) UCI machine learning repository
Atallah M, Bertino E, Elmagarmid A, Ibrahim M, Verykios V (1999) Disclosure limitation of sensitive rules. In: Proceedings 1999 workshop on knowledge and data engineering exchange (KDEX’99) (Cat. No.PR00453), pp 45–52. https://doi.org/10.1109/KDEX.1999.836532
B V (2019) Biomedical image analysis using semantic segmentation. J Innov Image Process 1:91–101. https://doi.org/10.36548/jiip.2019.2.004
Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press Inc, USA
Badhon B, Kabir MMJ, Xu S, Kabir M (2021) A survey on association rule mining based on evolutionary algorithms. Int J Comput Appl 43(8):775–785. https://doi.org/10.1080/1206212X.2019.1612993
Beasley D, Bull D, Martin R (1993) An overview of genetic algorithms: Pt1, fundamentals. Univer Comput 15:58–69
Berkhin P (2006) A survey of clustering data mining techniques. Springer, Berlin, Heidelberg, pp 25–71. https://doi.org/10.1007/3-540-28349-8_2
Berson A, Smith SJ (1997) Data warehousing, data mining, and olap, 1st edn. McGraw-Hill Inc, USA
Beume N, Naujoks B, Emmerich M (2007) Sms-emoa: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653–1669. https://doi.org/10.1016/j.ejor.2006.08.008. https://www.sciencedirect.com/science/article/pii/S0377221706005443
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132. https://doi.org/10.1016/j.knosys.2013.03.012. https://www.sciencedirect.com/science/article/pii/S0950705113001044
Burdick D, Calimlim M, Flannick J, Gehrke J, Yiu T (2005) Mafia: a maximal frequent itemset algorithm. IEEE Trans Knowl Data Eng 17(11):1490–1504. https://doi.org/10.1109/TKDE.2005.183
Cai X, Li Y, Fan Z, Zhang Q (2015) An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Trans Evol Comput 19(4):508–523. https://doi.org/10.1109/TEVC.2014.2350995
Cao H, Yang S, Wang Q, Wang Q, Zhang L (2019) A closed itemset property based multi-objective evolutionary approach for mining frequent and high utility itemsets. In: 2019 IEEE congress on evolutionary computation (CEC), pp 3356–3363. https://doi.org/10.1109/CEC.2019.8789985
Cattral R, Oppacher F, Graham KJL (2009) Techniques for evolutionary rule discovery in data mining. In: 2009 IEEE congress on evolutionary computation, pp 1737–1744. https://doi.org/10.1109/CEC.2009.4983151
Chan KCC, Au W-H (1997) An effective algorithm for mining interesting quantitative association rules. In: Proceedings of the 1997 ACM symposium on applied computing, SAC ’97, association for computing machinery, New York, NY, USA, p 88–90. https://doi.org/10.1145/331697.331714
Chan R, Yang Q, Shen Y-D (2003) Mining high utility itemsets. In: Third IEEE international conference on data mining, pp 19–26. https://doi.org/10.1109/ICDM.2003.1250893
Chen C-H, He J-S, Hong T-P (2013) A two-stage multi-objective genetic-fuzzy mining algorithm. In: 2013 IEEE international workshop on genetic and evolutionary fuzzy systems (GEFS), pp 16–20. https://doi.org/10.1109/GEFS.2013.6601050
Chen C-H, Hong T-P, Tseng VS, Lee C-S (2007) A genetic-fuzzy mining approach for items with multiple minimum supports. In: 2007 IEEE international fuzzy systems conference, pp 1–6. https://doi.org/10.1109/FUZZY.2007.4295628
Chen DC Daqing, Guo K (2012) Data mining for the online retail industry: A case study of rfm model-based customer segmentation using data mining. J Database Market Customer Strateg Manag 19:197–208. https://doi.org/10.1057/dbm.2012.17. https://link.springer.com/article/10.1057/dbm.2012.17
Chen M-S, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883. https://doi.org/10.1109/69.553155
Chen C-H, Hong T-P, Tseng VS (2009) An improved approach to find membership functions and multiple minimum supports in fuzzy data mining. Expert Syst Appl 36(6):10016–10024. https://doi.org/10.1016/j.eswa.2009.01.067
Chen C-H, Hong T-P, Lee Y-C (2012) Genetic-fuzzy mining with taxonomy. Int J Uncert Fuzz Knowl -Based Syst 20(supp02):187–205. https://doi.org/10.1142/S021848851240020X
Chen C-H, He J-S, Hong T-P (2013) Moga-based fuzzy data mining with taxonomy. Know.-Based Syst. 54(C):53–65
Cheng R, Jin Y, Narukawa K, Sendhoff B (2015) A multiobjective evolutionary algorithm using gaussian process-based inverse modeling. IEEE Trans Evol Comput 19(6):838–856. https://doi.org/10.1109/TEVC.2015.2395073
Cheng P, Pan J (2014) Association rule hiding based on evolutionary multi-objective optimization by removing items. In: Brodley CE, Stone P (Eds.), Proceedings of the twenty-eighth AAAI conference on artificial intelligence, July 27–31, 2014, Québec City, Québec, Canada, AAAI Press, pp. 3100–3101. http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8564
Chu C-J, Tseng VS, Liang T (2009) An efficient algorithm for mining high utility itemsets with negative item values in large databases. Appl Math Comput 215(2):767–778. https://doi.org/10.1016/j.amc.2009.05.066. https://www.sciencedirect.com/science/article/pii/S009630030900561X
Coello C Coello, Lechuga M (2002) Mopso: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation. CEC’02 (Cat. No.02TH8600), Vol 2, pp 1051–1056. https://doi.org/10.1109/CEC.2002.1004388
Coello C Coello (2006) Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput IntelL Magaz 1(1):28–36. https://doi.org/10.1109/MCI.2006.1597059
Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies
Daniel EL (1991) Knowledge discovery as a threat to database security. In: Proceedings of the 1st international conference on knowledge discovery and databases, Vol 107, p 516
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31. https://doi.org/10.1109/TEVC.2010.2059031
Dash M, Liu H (1997) Feature selection for classification. Intell Data Anal 1(1):131–156. https://doi.org/10.1016/S1088-467X(97)00008-5. https://www.sciencedirect.com/science/article/pii/S1088467X97000085
Das S, Mullick SS, Suganthan P (2016) Recent advances in differential evolution—an updated survey. Swarm Evol Comput 27:1–30. https://doi.org/10.1016/j.swevo.2016.01.004. https://www.sciencedirect.com/science/article/pii/S2210650216000146
Dasseni E, Verykios V, Elmagarmid A, Bertino E (2001) Hiding association rules by using confidence and support. Inf Hiding. https://doi.org/10.1007/3-540-45496-9_27
Dawar S, Goyal V (2015) Up-hist tree: An efficient data structure for mining high utility patterns from transaction databases. In: Proceedings of the 19th international database engineering and applications symposium, IDEAS ’15, association for computing machinery, New York, NY, USA, p 56–61. https://doi.org/10.1145/2790755.2790771
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017
Dong J, Han M (2007) Bittablefi: An efficient mining frequent itemsets algorithm. Knowl -Based Syst 20(4):329–335. https://doi.org/10.1016/j.knosys.2006.08.005. https://www.sciencedirect.com/science/article/pii/S0950705106001493
Dorigo M, Gambardella L (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66. https://doi.org/10.1109/4235.585892
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41. https://doi.org/10.1109/3477.484436
Dunning LA, Kresman R (2013) Privacy preserving data sharing with anonymous id assignment. IEEE Trans Inf Forens Secur 8(2):402–413. https://doi.org/10.1109/TIFS.2012.2235831
Duong Q-H, Liao B, Fournier-Viger P, Dam T-L (2016) An efficient algorithm for mining the top-k high utility itemsets, using novel threshold raising and pruning strategies. Knowl -Based Syst 104(C):106–122. https://doi.org/10.1016/j.knosys.2016.04.016
Engelbrecht A, Pampara G (2007) Binary differential evolution strategies. In: 2007 IEEE congress on evolutionary computation, pp 1942–1947. https://doi.org/10.1109/CEC.2007.4424711
Erwin A, Gopalan RP, Achuthan N (2007) Ctu-mine: an efficient high utility itemset mining algorithm using the pattern growth approach. In: 7th IEEE international conference on computer and information technology (CIT 2007), pp 71–76. https://doi.org/10.1109/CIT.2007.120
Fang W, Zhang Q, Sun J, Wu X-J (2020) Mining high quality patterns using multi-objective evolutionary algorithm. IEEE Trans Knowl Data Eng 1–1. https://doi.org/10.1109/TKDE.2020.3033519
Faramondi L, Oliva G, Panzieri S, Pascucci F, Schlueter M, Munetomo M, Setola R (2019) Network structural vulnerability: a multiobjective attacker perspective. IEEE Trans Syst Man Cybern Syst 49(10):2036–2049. https://doi.org/10.1109/TSMC.2018.2790438
Fieldsend J, Singh S (2005) Pareto evolutionary neural networks. IEEE Trans Neural Netw 16(2):338–354. https://doi.org/10.1109/TNN.2004.841794
Fournier-Viger RNBV, Philippe, Lin JC-W, Tseng VS (2019) High-utility pattern mining—theory, algorithms and applications, 1st Edition, Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8. https://link.springer.com/book/10.1007/978-3-030-04921-8
Fournier-Viger P, Wu C-W, Zida S, Tseng VS (2014) Fhm: faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen T, Christiansen H, Cubero J-C, Raś ZW (eds) Found Intel Syst. Springer, Cham, pp 83–92
Fournier-Viger P, Lin JC-W, Gomariz A, Gueniche T, Soltani A, Deng Z, Lam HT (2016) The spmf open-source data mining library version 2. In: Berendt B, Bringmann B, Fromont É, Garriga G, Miettinen P, Tatti N, Tresp V (eds) Mach Learn Knowl Discov Databases. Springer, Cham, pp 36–40
Freitas AA (2003) A survey of evolutionary algorithms for data mining and knowledge discovery. Springer, Berlin, Heidelberg, pp 819–845. https://doi.org/10.1007/978-3-642-18965-4_33
Gade K, Wang J, Karypis G (2004) Efficient closed pattern mining in the presence of tough block constraints. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04, association for computing machinery, New York, NY, USA, p 138–147. https://doi.org/10.1145/1014052.1014070
Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Tseng VS, Yu PS (2021) A survey of utility-oriented pattern mining. IEEE Trans Knowl Data Eng 33(4):1306–1327. https://doi.org/10.1109/TKDE.2019.2942594
Ganesan M, Shankar S (2021) High utility fuzzy product mining (hufpm) using investigation of huwas approach. J Amb Intell Human Comput, 1–11 https://doi.org/10.1007/s12652-021-03231-8. https://link.springer.com/article/10.1007/s12652-021-03231-8
Goethals B (2003) Frequent itemset mining dataset repository. Freq Itemset Min Implement (FIMI’03)
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99. https://doi.org/10.1023/A:1022602019183. https://link.springer.com/article/10.1023/A:1022602019183
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co. Inc, USA
Goyal V, Dawar S, Sureka A (2015) High utility rare itemset mining over transaction databases, pp 27–40. https://doi.org/10.1007/978-3-319-16313-0_3
Guidotti AMDP, Riccardo, Gabrielli L, Giannotti F Discovering temporal regularities in retail customers’ shopping behavior. EPJ Data Sci 7(6). https://doi.org/10.1140/epjds/s13688-018-0133-0. https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-0
Guo-Cheng Lan T-PH, Tseng VS (2014) An efficient projection-based indexing approach for mining high utility itemsets. Knowl Inf Syst 38:85–107. https://doi.org/10.1007/s10115-012-0492-y. https://link.springer.com/article/10.1007/s10115-012-0492-y
Gupta D, Chauhan AS (2015) Ant colony based optimization from infrequent itemsets. In: Satapathy SC, Biswal BN, Udgata SK, Mandal J (eds) Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA) 2014. Springer, Cham, pp 797–804
Haglin D, Manning A (2007) On minimal infrequent itemset mining, pp 141–147
Hamdani TM, Won J-M, Alimi AM, Karray F (2007) Multi-objective feature selection with NSGA ii. In: Beliczynski B, Dzielinski A, Iwanowski M, Ribeiro B (eds) Adapt Nat Comput Algorith. Springer, Berlin, Heidelberg, pp 240–247
Han DXJ, Cheng H, Yan X (2007) Frequent pattern mining: current status and future directions. Data Mining Knowl Disc 15:55–86. https://doi.org/10.1007/s10618-006-0059-1. https://link.springer.com/article/10.1007/s10618-006-0059-1
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. SIGMOD Rec 29(2):1–12. https://doi.org/10.1145/335191.335372
Han S, Ng WK (2007) Privacy-preserving genetic algorithms for rule discovery. In: Proceedings of the 9th international conference on data warehousing and knowledge discovery, DaWaK’07, Springer, Berlin, Heidelberg, p 407–417
Holland J Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control Artif Intell
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge, MA, USA
Hong T-P, Chiang P-H, Huang W-M, Lin JC-W (2019) Fuzzy utility mining under minimum weight constraint of multiple item weights. In: 2019 international conference on data mining workshops (ICDMW), pp 982–986. https://doi.org/10.1109/ICDMW.2019.00142
Hong T-P, Lin C-Y, Huang W-M, Li S-M, Wang S-L, Lin JC-W (2019) Mining temporal fuzzy utility itemsets by tree structure. In: 2019 IEEE international conference on big data (big data), pp 2659–2663. https://doi.org/10.1109/BigData47090.2019.9006317
Hong T-P, Lin K-Y, Wang S-L (2003) Fuzzy data mining for interesting generalized association rules. Fuzzy Sets Syst 138(2):255–269. https://doi.org/10.1016/S0165-0114(02)00272-5. https://www.sciencedirect.com/science/article/pii/S0165011402002725
Hong T-P, Wang C-Y Maintenance of association rules using pre-large itemsets https://doi.org/10.4018/978-1-59904-120-9.ch003
Hong T-P, Kuo C-S, Chi S-C (1999) Mining association rules from quantitative data. Intell Data Anal 3(5):363–376. https://doi.org/10.1016/S1088-467X(99)00028-1
Hong T-P, Wang C-Y, Tao Y-H (2001) A new incremental data mining algorithm using pre-large itemsets. Intell Data Anal 5(2):111–129
Hong T-P, Chen C-H, Wu Y-L, Lee Y-C (2006) A ga-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput 10(11):1091–1101. https://doi.org/10.1007/s00500-006-0046-x
Hong T-P, Lan G-C, Lin Y-H, Pan S-T (2013) An effective gradual data-reduction strategy for fuzzy itemset mining. Int J Fuzzy Syst 15(2):170–181
Hong T-P, Lin C-W, Yang K-T, Wang S-L (2013) Using tf-idf to hide sensitive itemsets. Appl Intell 38(4):502–510. https://doi.org/10.1007/s10489-012-0377-5
Hong T-P, Lin C-W, Lin T-C (2014) The mffp-tree fuzzy mining algorithm to discover complete linguistic frequent itemsets. Comput Intell 30(1):145–166. https://doi.org/10.1111/j.1467-8640.2012.00467.x
Hong T-P, Ku M-P, Chiu H-W, Huang W-M, Li S-M, Lin JC-W (2021) A single-stage tree-structure-based approach to determine fuzzy average-utility itemsets. In: Fujita H, Selamat A, Lin JC-W, Ali M (eds) Advances and trends in artificial intelligence. artificial intelligence practices. Springer, Cham, pp 66–72
Hong T, Ku M, Huang W, Li S, Lin JC (2020) A tree-based fuzzy average-utility mining algorithm. In: Fatta GD, Sheng VS, Cuzzocrea A, Zaniolo C, Wu X (Eds.), 20th international conference on data mining workshops, ICDM workshops 2020, Sorrento, Italy, November 17–20, 2020, IEEE, pp 669–672. https://doi.org/10.1109/ICDMW51313.2020.00094
Hooshsadat M, Bayat S, Naimi P, Mirian MS, Zaïane OR (2012) UAPRIORI: an algorithm for finding sequential patterns in probabilistic data. World scientific proceedings series on computer engineering and information science, pp. 907–912. https://doi.org/10.1142/9789814417747_0145. https://www.worldscientific.com/doi/abs/10.1142/9789814417747_0145
Huang W-M, Hong T-P, Lin JC-W, Chiang M-C (2018) Temporal fuzzy utility mining with multiple minimum utility thresholds. In: 1st international workshop on utility-driven mining
Huang W-M, Hong T-P, Lan G-C, Chiang M-C, Lin JC-W (2017) Temporal-based fuzzy utility mining. IEEE Access 5:26639–26652. https://doi.org/10.1109/ACCESS.2017.2774510
Huang W-M, Hong T-P, Chiang M-C, Lin JC-W (2019) Using multi-conditional minimum thresholds in temporal fuzzy utility mining. Int J Comput Intell Syst 12:613–626. https://doi.org/10.2991/ijcis.d.190426.001
Hu J, Mojsilovic A (2007) High-utility pattern mining: a method for discovery of high-utility item sets. Pattern Recogn 40(11):3317–3324. https://doi.org/10.1016/j.patcog.2007.02.003. https://www.sciencedirect.com/science/article/pii/S0031320307000866
Ishibuchi H, Tsukamoto N, Nojima Y (2010) Diversity improvement by non-geometric binary crossover in evolutionary multiobjective optimization. IEEE Trans Evol Comput 14(6):985–998. https://doi.org/10.1109/TEVC.2010.2043365
Javangula V et al (2021) Mining of high utility item sets using genetic algorithm. Turkish J Comput Math Educ (TURCOMAT) 12(9):2437–2448
Lin PFVTPHJMTWJCH, Li T, Zhan J(2017) Efficient mining of multiple fuzzy frequent itemsets. Int J Fuzzy Syst 19:1032–1040. https://doi.org/10.1007/s40815-016-0246-1. https://link.springer.com/article/10.1007/s40815-016-0246-1
Jerry PFVTPH, Chun-Wei L, Lu Y, Voznak M (2017) A binary pso approach to mine high-utility itemsets. Soft Comput 21:5103–5121. https://doi.org/10.1007/s00500-016-2106-1. https://link.springer.com/article/10.1007/s00500-016-2106-1
Jin Y, Okabe T, Sendhoff B (2004) Evolutionary multi-objective optimization approach to constructing neural network ensembles for regression. In: Advances in natural computation—applications of multi-objective evolutionary algorithms, World Scientific, pp. 635–673. https://doi.org/10.1142/9789812567796_0027
Kaelo P, Ali M (2006) A numerical study of some modified differential evolution algorithms. Eur J Oper Res 169(3):1176–1184. https://doi.org/10.1016/j.ejor.2004.08.047. https://www.sciencedirect.com/science/article/pii/S037722170500281X
Kalaipriyan TVSSP , Sourabh P, Thirumal V (2019) Reinforced cuckoo search algorithm-based multimodal optimization. Appl Intell 49(6):2059–2083. https://doi.org/10.1007/s10489-018-1355-3. https://link.springer.com/article/10.1007/s10489-018-1355-3
Kannimuthu S, Premalatha K (2014) Discovery of high utility itemsets using genetic algorithm with ranked mutation. Appl Artif Intell 28(4):337–359
Karaboga D An idea based on honey bee swarm for numerical optimization, technical report—tr06, Technical Report, Erciyes University
Karakatič S, Podgorelec V (2015) A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl Soft Comput 27(C):519–532. https://doi.org/10.1016/j.asoc.2014.11.005
Kaya M (2005) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput 10:578–586. https://doi.org/10.1007/s00500-005-0509-5. https://link.springer.com/article/10.1007/s00500-005-0509-5
Kaya M, Alhajj R (2005) Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst 152(3):587–601. https://doi.org/10.1016/j.fss.2004.09.014
Kaya M, Alhajj R (2006) Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rules mining. Appl Intell 24(1):7–15. https://doi.org/10.1007/s10489-006-6925-0
Kazimipour B, Li X, Qin AK (2014) A review of population initialization techniques for evolutionary algorithms. In: 2014 IEEE congress on evolutionary computation (CEC), pp 2585–2592. https://doi.org/10.1109/CEC.2014.6900618
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, Vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kennedy J, Eberhart R (1997) A discrete binary version of the particle swarm algorithm. In: 1997 IEEE international conference on systems, man, and cybernetics. Comput Cybern Simul Vol 5, pp 4104–4108. https://doi.org/10.1109/ICSMC.1997.637339
Krishna GJ, Ravi V (2020) Mining top high utility association rules using binary differential evolution. Eng Appl Artif Intell 96:103935. https://doi.org/10.1016/j.engappai.2020.103935. https://www.sciencedirect.com/science/article/pii/S0952197620302578
Kumar V, Thakur RS (2018) High fuzzy utility strategy based webpage sets mining from weblog database. Int J Intell Eng Syst 11(1):191–200
Kuo R, Chao C, Chiu Y (2011) Application of particle swarm optimization to association rule mining. Appl Soft Comput 11(1):326–336. https://doi.org/10.1016/j.asoc.2009.11.023. https://www.sciencedirect.com/science/article/pii/S1568494609002361
Kuok CM, Fu A, Wong MH (1998) Mining fuzzy association rules in databases. SIGMOD Rec 27(1):41–46. https://doi.org/10.1145/273244.273257
Lalou M, Tahraoui MA, Kheddouci H (2018) The critical node detection problem in networks: a survey. Compu Sci Rev 28:92–117. https://doi.org/10.1016/j.cosrev.2018.02.002. https://www.sciencedirect.com/science/article/pii/S1574013716302416
Lan G-C, Hong T-P, Lin Y-H, Wang S-L (2014) Fast discovery of high fuzzy utility itemsets. In: 2014 IEEE international conference on systems, man, and cybernetics (SMC), pp 2764–2767. https://doi.org/10.1109/SMC.2014.6974346
Lan G-C, Hong T-P, Lin Y-H, Wang S-L (2015) Fuzzy utility mining with upper-bound measure. Appl Soft Comput 30:767–777. https://doi.org/10.1016/j.asoc.2015.01.055. https://www.sciencedirect.com/science/article/pii/S1568494615000769
Lee Y-C, Hong T-P, Wang T-C (2008) Multi-level fuzzy mining with multiple minimum supports. Exp Syst Appl 34(1):459–468. https://doi.org/10.1016/j.eswa.2006.09.011. https://www.sciencedirect.com/science/article/pii/S0957417406002958
Levesque J-C, Durand A, Gagne C, Sabourin R (2012) Multi-objective evolutionary optimization for generating ensembles of classifiers in the roc space. In: Proceedings of the 14th annual conference on genetic and evolutionary computation, GECCO ’12, association for computing machinery, New York, NY, USA, p 879–886. https://doi.org/10.1145/2330163.2330285
Li F-g, Sun Y-j, Ni Z-w, Liang Y, Mao X-m (2012) The utility frequent pattern mining based on slide window in data stream. In: 2012 fifth international conference on intelligent computation technology and automation, pp 414–419. https://doi.org/10.1109/ICICTA.2012.110
LI ZJS Xiao Lei, QIAN JX (2002) An optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng-Theory Pract 22(11):32–38. https://doi.org/10.12011/1000-6788(2002)11-32. http://www.sysengi.com/EN/Y2002/V22/I11/32
Li H, Zhang Q, Deng J, Xu Z (2018) A preference-based multiobjective evolutionary approach for sparse optimization. IEEE Trans Neural Netw Learn Syst 29(5):1716–1731. https://doi.org/10.1109/TNNLS.2017.2677973
Lin C-W, Hong T-P, Lu W-H. An efficient tree-based fuzzy data mining approach. Int J Fuzzy Syst 12
Lin C-W, Hong T-P, Lu W-H (2009) The pre-fufp algorithm for incremental mining. Exp Syst Appl 36(5):9498–9505. https://doi.org/10.1016/j.eswa.2008.03.014. https://www.sciencedirect.com/science/article/pii/S0957417408001826
Lin C-W, Hong T-P (2013) A survey of fuzzy web mining, Wiley interdisciplinary reviews. Data mining and knowledge discovery 3(3):190–199. https://doi.org/10.1002/widm.1091. https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1091
Lin C-W, Hong T-P, Wong J-W, Lan G-C (2013) Privacy preserving high utility mining based on genetic algorithms. In: 2013 IEEE international conference on granular computing (GrC), pp 191–195. https://doi.org/10.1109/GrC.2013.6740406
Lin C-W, Hong T-P, Wong J-W, Lan g-c, Lin W-Y (2014) A ga-based approach to hide sensitive high utility itemsets. Sci World J 2014, 804629. https://doi.org/10.1155/2014/804629. https://www.hindawi.com/journals/tswj/2014/804629/
Lin C-W, Yang L, Viger P Fournier, Frnda J, Sevcik L, Vozňák M An evolutionary algorithm to mine high-utility itemsets. Adv Electr Electron Eng 13. https://doi.org/10.15598/aeee.v13i4.1474
Lin C-W, Yang L, Viger P Fournier, Wu M-T, Hong T-P, Wang L-L (2015) A swarm-based approach to mine high-utility itemsets, Vol 540, pp 572–581. https://doi.org/10.1007/978-3-662-48319-0_48
Lin C-W, Yuyu Z, Zhang B, Viger P Fournier, Djenouri Y Hiding sensitive itemsets with multiple objective optimization. Soft Comput 23. https://doi.org/10.1007/s00500-019-03829-3
Lin JC-W, Gan W, Fournier-Viger P, Hong T-P (2015) Rwfim: recent weighted-frequent itemsets mining. Eng Appl Artif Intell 45:18–32. http://dblp.uni-trier.de/db/journals/eaai/eaai45.html#LinGFH15
Lin JC-W, Gan W, Fournier-Viger P, Hong T-P, Tseng VS (2015) Mining high-utility itemsets with various discount strategies. In: 2015 IEEE international conference on data science and advanced analytics (DSAA), pp 1–10. https://doi.org/10.1109/DSAA.2015.7344861
Lin JC-W, Gan W, Fournier-Viger P, Yang L, Liu Q, Frnda J, Sevcik L, Voznak M (2016) High utility-itemset mining and privacy-preserving utility mining. Perspect Sci 7:74–80, 1st Czech-China scientific conference 2015. https://doi.org/10.1016/j.pisc.2015.11.013. https://www.sciencedirect.com/science/article/pii/S2213020915000580
Lin JC-W, Hong T-P, Lin T-C (2015) A cmffp-tree algorithm to mine complete multiple fuzzy frequent itemsets. Appl Soft Comput 28:431–439. https://doi.org/10.1016/j.asoc.2014.11.049. https://www.sciencedirect.com/science/article/pii/S1568494614006139
Lin C-W, Hong T-P (2014) Mining fuzzy frequent itemsets based on ubffp trees. J Intell Fuzzy Syst 27(1):535–548. https://doi.org/10.3233/IFS-131022
Lin C-W, Hong T-P, Chang C-C, Wang S-L (2013) A greedy-based approach for hiding sensitive itemsets by transaction insertion. J Inf Hiding Multimed Signal Process 4:201–214
Lin C-W, Zhang B, Yang K-T, Hong T-P (2014) Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms. Sci World J 2014:398269. https://doi.org/10.1155/2014/398269
Lin C-W, Li T, Viger P Fournier, Hong T-P (2015) A fast algorithm for mining fuzzy frequent itemsets. J Intell Fuzzy Syst 29:2373–2379. https://doi.org/10.3233/IFS-151936
Lin C-W, Hong T-P, Lin T-C, Pan S-T (2015) An ubmffp tree for mining multiple fuzzy frequent itemsets. Int J Uncert Fuzz Knowl -Based Syst 23:861–879. https://doi.org/10.1142/S0218488515500385
Lin C-W, Hong T-P, Yang K-T, Wang S-L (2015) The ga-based algorithms for optimizing hiding sensitive itemsets through transaction deletion. Appl Intell 42(2):210–230. https://doi.org/10.1007/s10489-014-0590-5
Lin JC-W, Gan W, Fournier-Viger P, Hong T-P, Zhan J (2016) Efficient mining of high-utility itemsets using multiple minimum utility thresholds. Know -Based Syst 113(C):100–115. https://doi.org/10.1016/j.knosys.2016.09.013
Lin JC-W, Yang L, Fournier-Viger P, Wu JM-T, Hong T-P, Wang LS-L, Zhan J (2016) Mining high-utility itemsets based on particle swarm optimization. Eng Appl Artif Intell 55(C):320–330. https://doi.org/10.1016/j.engappai.2016.07.006
Lin JC-W, Gan W, Fournier-Viger P, Hong T-P, Chao H-C (2017) Mining weighted frequent itemsets without candidate generation in uncertain databases. Int J Inf Technol Decis Making 16(06):1549–1579. https://doi.org/10.1142/S0219622017500341
Lindell Y, Pinkas B (2000) Privacy preserving data mining. In: Annual international cryptology conference, Springer, pp 36–54
Liu Y, Liao W-K, Choudhary A (2005) A two-phase algorithm for fast discovery of high utility itemsets. In: Ho TB, Cheung D, Liu H (eds) Advances in knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 689–695
Liu Q, Ge Y, Li Z, Chen E, Xiong H (2011) Personalized travel package recommendation. In: 2011 IEEE 11th international conference on data mining, pp 407–416. https://doi.org/10.1109/ICDM.2011.118
Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’99, association for computing machinery, New York, NY, USA, p 337–341. https://doi.org/10.1145/312129.312274
Liu M, Qu J (2012) Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM international conference on information and knowledge management, CIKM ’12, association for computing machinery, New York, NY, USA, p 55–64. https://doi.org/10.1145/2396761.2396773
Li Y, Wang S, Pan Q, Peng H, Yang T, Cambria E (2019) Learning binary codes with neural collaborative filtering for efficient recommendation systems. Knowl -Based Syst 172:64–75. https://doi.org/10.1016/j.knosys.2019.02.012. https://www.sciencedirect.com/science/article/pii/S0950705119300735
Li X, Yin M (2016) A particle swarm inspired cuckoo search algorithm for real parameter optimization. Soft Comput 20:1389–1413. https://doi.org/10.1007/s00500-015-1594-8. https://link.springer.com/article/10.1007/s00500-015-1594-8
Ma X, Liu F, Qi Y, Wang X, Li L, Jiao L, Yin M, Gong M (2016) A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Trans Evol Comput 20(2):275–298. https://doi.org/10.1109/TEVC.2015.2455812
Menhas MI, Fei M, Wang L, Fu X (2011) A novel hybrid binary pso algorithm, ICSI’11. Springer, Berlin, Heidelberg, pp 93–100
Miettinen K (2012) Nonlinear multiobjective optimization, Vol 12, Springer
Minaei-Bidgoli B, Barmaki R, Nasiri M (2013) Mining numerical association rules via multi-objective genetic algorithms. Inf Sci 233:15–24. https://doi.org/10.1016/j.ins.2013.01.028. https://www.sciencedirect.com/science/article/pii/S0020025513001072
Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl -Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006. https://www.sciencedirect.com/science/article/pii/S0950705115002580
Mirjalili S, Dong J Song, Sadiq AS, Faris H (2020) Genetic algorithm: theory, literature review, and application in image reconstruction. Springer, Cham, pp 69–85. https://doi.org/10.1007/978-3-030-12127-3_5
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007. https://www.sciencedirect.com/science/article/pii/S0965997813001853
Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello CAC (2014) A survey of multiobjective evolutionary algorithms for data mining: part i. IEEE Trans Evol Comput 18(1):4–19. https://doi.org/10.1109/TEVC.2013.2290086
Mukhopadhyay A, Maulik U, Bandyopadhyay S A survey of multiobjective evolutionary clustering, ACM Comput Surv 47(4). https://doi.org/10.1145/2742642
Najafabadi MK, Mohamed AH, Mahrin MN (2019) A survey on data mining techniques in recommender systems. Soft Comput 23(2):627–654. https://doi.org/10.1007/s00500-017-2918-7. https://link.springer.com/article/10.1007/s00500-017-2918-7
Nouaouria N, Boukadoum M, Proulx R (2013) Particle swarm classification: a survey and positioning. Pattern Recog 46(7):2028–2044. https://doi.org/10.1016/j.patcog.2012.12.011. https://www.sciencedirect.com/science/article/pii/S0031320313000022
Oliveira SRM, Zaïane OR (2002) Privacy preserving frequent itemset mining. In: Proceedings of the IEEE international conference on privacy, security and data mining, Vol 14, CRPIT ’14, Australian Computer Society, Inc., AUS, p 43–54
Ouyang W, Huang Q (2011) Mining direct and indirect fuzzy association rules with multiple minimum supports in large transaction databases. In: 2011 eighth international conference on fuzzy systems and knowledge discovery (FSKD), Vol 2, pp 947–951. https://doi.org/10.1109/FSKD.2011.6019747
Ozturk C, Hancer E, Karaboga D (2015) A novel binary artificial bee colony algorithm based on genetic operators. Inf Sci 297(C):154–170. https://doi.org/10.1016/j.ins.2014.10.060
Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Exp Syst Appl 39(11):10059–10072. https://doi.org/10.1016/j.eswa.2012.02.038. https://www.sciencedirect.com/science/article/pii/S0957417412002825
Pazhaniraja SSN, Kumar BS (2020) High utility itemset mining: a boolean operators-based modified grey wolf optimization algorithm. Soft Comput 24(21):16691–16704. https://doi.org/10.1007/s00500-020-05123-z. https://link.springer.com/article/10.1007/s00500-020-05123-z
Pazhaniraja N, Sountharrajan S (2021) High utility itemset mining using dolphin echolocation optimization. J Amb Intell Human Comput 12:8413–8426. https://doi.org/10.1007/s12652-020-02571-1. https://link.springer.com/article/10.1007/s12652-020-02571-1
Pears R, Koh YS (2011) Weighted association rule mining using particle swarm optimization. In: Proceedings of the 15th international conference on new frontiers in applied data mining, PAKDD’11, Springer, Berlin, Heidelberg, p 327–338. https://doi.org/10.1007/978-3-642-28320-8_28
Pillai J, Vyas OP, Muyeba M (2013) Huri—a novel algorithm for mining high utility rare itemsets. In: Meghanathan N, Nagamalai D, Chaki N (eds) Adv Comput Inf Technol. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 531–540
Pillai J, Vyas OP CSHURI—modified HURI algorithm for customer segmentation and transaction profitability. CoRR arXiv:1205.1609
Ponsich A, Jaimes AL, Coello CAC (2013) A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans Evol Comput 17(3):321–344. https://doi.org/10.1109/TEVC.2012.2196800
Projection CIQDM (1996)
Qu J-F, Liu M, Viger P Fournier (2019) Efficient algorithms for high utility itemset mining without candidate generation, pp 131–160. https://doi.org/10.1007/978-3-030-04921-8_5
Ross Quinlan R, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Wu X, Kumar V, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37. https://doi.org/10.1007/s10115-007-0114-2. https://link.springer.com/article/10.1007/s10115-007-0114-2
Salleb A, Vrain C, Nortet C (2007) Quantminer: a genetic algorithm for mining quantitative association rules, Vol 7, pp 1035–1040
Sarath K, Ravi V (2013) Association rule mining using binary particle swarm optimization. Eng Appl Artif Intell 26(8):1832–1840. https://doi.org/10.1016/j.engappai.2013.06.003. https://www.sciencedirect.com/science/article/pii/S0952197613001048
Seidlova R, Poživil J, Seidl J (2019) Marketing and business intelligence with help of ant colony algorithm. J Strategic Mark 27(5):451–463. https://doi.org/10.1080/0965254X.2018.1430058
Simões A, Costa E (1999) Transposition: a biological-inspired mechanism to use with genetic algorithms. In: Dobnikar A, Steele NC, Pearson DW, Albrecht RF (Eds.), Proceedings of the international conference on artificial neural nets and genetic algorithms, ICANNGA 1999, Portorož, Slovenia, 1999, Springer, pp 178–186. https://doi.org/10.1007/978-3-7091-6384-9_31
Singh SS, Kumar A, Singh K, Biswas B (2020) Im-sso: maximizing influence in social networks using social spider optimization. Concurr Comput Pract Exper 32(2):e5421. https://doi.org/10.1002/cpe.5421
Singh K, Shakya HK, Singh A, Biswas B (2018) Mining of high-utility itemsets with negative utility. Exp Syst 35(6):e12296–e12296 https://doi.org/10.1111/exsy.12296. https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12296
Song W, Huang C (2018) Mining high utility itemsets using bio-inspired algorithms: a diverse optimal value framework. IEEE Access 6:19568–19582. https://doi.org/10.1109/ACCESS.2018.2819162
Song W, Li J (2020) Discovering high utility itemsets using set-based particle swarm optimization. In: Yang X, Wang C-D, Islam MS, Zhang Z (eds) Advanced data mining and applications. Springer, Cham, pp 38–53
Song W, Yang B, Xu Z (2008) Index-bittablefi: an improved algorithm for mining frequent itemsets. Knowl -Based Syst 21:507–513. https://doi.org/10.1016/j.knosys.2008.03.011
Song W, Liu Y, Li J (2014) Bahui: fast and memory efficient mining of high utility itemsets based on bitmap. Int J Data Warehous Min 10(1):1–15. https://doi.org/10.4018/ijdwm.2014010101
Song A, Ding X, Chen J, Li M, Cao W, Pu K (2016) Multi-objective association rule mining with binary bat algorithm. Intell Data Anal 20:105–128. https://doi.org/10.3233/IDA-150796
Song W, Li J, Huang C (2021) Artificial fish swarm algorithm for mining high utility itemsets. In: Tan Y, Shi Y (eds) Advances in swarm intelligence. Springer, Cham, pp 407–419
Song W, Huang C (2018) Discovering high utility itemsets based on the artificial bee colony algorithm, pp 3–14. https://doi.org/10.1007/978-3-319-93040-4_1
Soto R, Crawford B, Carrasco C, Almonacid B, Reyes V, Araya I, Misra S, Olguín E (2016) Solving manufacturing cell design problems by using a dolphin echolocation algorithm. In: Misra S, Gervasi O, Torre C, Taniar D, Stankova E, Rocha A, Wang S, Murgante B, Apduhan B (eds) Computational science and its applications—16th international conference, ICCSA 2016, proceedings, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture Notes in Bioinformatics), Springer, pp 77–86, publisher Copyright: Springer, Switzerland 2016.; null ; conference date: 04-07-2016 Through 07-07-2016. https://doi.org/10.1007/978-3-319-42092-9_7
Srikant R (1999) Ibm quest synthetic data generation code, not available online anymore
Srivastava G, Lin JC-W, Pirouz M, Li Y, Yun U (2021) A pre-large weighted-fusion system of sensed high-utility patterns. IEEE Sens J 21(14):15626–15634. https://doi.org/10.1109/JSEN.2020.2991045
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328. https://link.springer.com/article/10.1023/A:1008202821328
Tang L, Zhang L, Luo P, Wang M (2012) Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management, CIKM ’12, ssociation for Computing Machinery, New York, NY, USA, p 75–84. https://doi.org/10.1145/2396761.2396775
Telikani A, Gandomi AH, Shahbahrami A (2020) A survey of evolutionary computation for association rule mining. Inf Sci 524:318–352. https://doi.org/10.1016/j.ins.2020.02.073. https://www.sciencedirect.com/science/article/pii/S002002552030164X
Tian Y, Zhang X, Wang C, Jin Y (2020) An evolutionary algorithm for large-scale sparse multiobjective optimization problems. IEEE Trans Evol Comput 24(2):380–393. https://doi.org/10.1109/TEVC.2019.2918140
Tian D, Gledson A, Antoniades A, Aristodimou A, Dimitrios N, Sahay R, Pan J, Stivaros S, Nenadic G, Zeng X-j, Keane J (2013) A bayesian association rule mining algorithm. In: 2013 IEEE international conference on systems, man, and cybernetics, pp 3258–3264. https://doi.org/10.1109/SMC.2013.555
Tsai C-S, Chun-Wei, Huang K-W, Chiang M-C (2015) A fast particle swarm optimization for clustering. Soft Comput 19(2):321–338. https://doi.org/10.1007/s00500-014-1255-3. https://link.springer.com/article/10.1007/s00500-014-1255-3
Tseng VS, Wu C-W, Shie B-E, Yu PS (2010) Up-growth: An efficient algorithm for high utility itemset mining. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’10, association for computing machinery, New York, NY, USA, p 253–262. https://doi.org/10.1145/1835804.1835839
Tseng VS, Shie B-E, Wu C-W, Yu PS (2013) Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans Knowl Data Eng 25(8):1772–1786. https://doi.org/10.1109/TKDE.2012.59
Tseng VS, Wu C-W, Fournier-Viger P, Yu PS (2015) Efficient algorithms for mining the concise and lossless representation of high utility itemsets. IEEE Trans Knowl Data Eng 27(3):726–739. https://doi.org/10.1109/TKDE.2014.2345377
Venkatadri M, Rao KS (2010) A multiobjective genetic algorithm for feature selection in data mining. Int J Comput Sci Inf Technol 1(5):443–448
Ventresca M, Harrison KR, Ombuki-Berman BM (2018) The bi-objective critical node detection problem. Eur J Oper Res 265(3):895–908. https://doi.org/10.1016/j.ejor.2017.08.053. https://www.sciencedirect.com/science/article/pii/S0377221717307841
Ventura S, Luna JM (2016) Pattern mining with evolutionary algorithms, 1st edn., Springer. https://doi.org/10.1007/978-3-319-33858-3. https://www.springer.com/gp/book/9783319338576
Verykios VS, Bertino E, Fovino IN, Provenza LP, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. SIGMOD Rec. 33(1):50–57. https://doi.org/10.1145/974121.974131
Vu KK, d’Ambrosio C, Hamadi Y, Liberti L (2017) Surrogate-based methods for black-box optimization. Int Trans Oper Res 24(3):393–424
Wang C-M, Chen S-H, Huang Y-F (2009) A fuzzy approach for mining high utility quantitative itemsets. In: 2009 IEEE international conference on fuzzy systems, pp 1909–1913. https://doi.org/10.1109/FUZZY.2009.5277408
Wang J-S, Li S-X (2019) An improved grey wolf optimizer based on differential evolution and elimination mechanism. Sci Rep 9(1):1–21, article number: 7181. https://doi.org/10.1038/s41598-019-43546-3. https://www.nature.com/articles/s41598-019-43546-3
Wang Z, Hutter F, Zoghi M, Matheson D, De Freitas N (2016) Bayesian optimization in a billion dimensions via random embeddings. J Artif Int Res 55(1):361–387
Wang Z, Zhang Q, Zhou A, Gong M, Jiao L (2016) Adaptive replacement strategies for moea/d. IEEE Trans Cybern 46(2):474–486. https://doi.org/10.1109/TCYB.2015.2403849
Wenyin GZC, Ling CX (2010) De/bbo: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15:645–665. https://doi.org/10.1007/s00500-010-0591-1. https://link.springer.com/article/10.1007/s00500-010-0591-1
Wu JM-T, Lin J Chun-Wei, Djenouri Y, Fournier-Viger P, Zhang Y (2019) A swarm-based data sanitization algorithm in privacy-preserving data mining. In: 2019 IEEE congress on evolutionary computation (CEC), pp 1461–1467. https://doi.org/10.1109/CEC.2019.8790271
Wu JM-T, Lin JC-W, Fournier-Viger P, Wiktorski T, Hong T-P, Pirouz M (2019) A ga-based framework for mining high fuzzy utility itemsets. In: 2019 IEEE international conference on big data (big data), pp 2708–2715. https://doi.org/10.1109/BigData47090.2019.9006171
Wu JM-T, Zhan J, Lin JC-W (2016) Mining of high-utility itemsets by aco algorithm. In: Proceedings of the the 3rd multidisciplinary international social networks conference on social informatics 2016, data science 2016, MISNC, SI, DS 2016, Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2955129.2955179
Wu T-Y, Lin JC-W, Zhang Y, Chen C-H. A grid-based swarm intelligence algorithm for privacy-preserving data mining. Appl Sci 9(4). https://doi.org/10.3390/app9040774. https://www.mdpi.com/2076-3417/9/4/774
Wu JM-T, Zhan J, Lin JC-W (2017) An aco-based approach to mine high-utility itemsets. Know -Based Syst 116(C):102–113. https://doi.org/10.1016/j.knosys.2016.10.027
Wu C, He Y (2020) Solving the set-union knapsack problem by a novel hybrid jaya algorithm. Soft Comput 24(3):1883–1902. https://doi.org/10.1007/s00500-019-04021-3. https://link.springer.com/article/10.1007/s00500-019-04021-3
Xiao S, Hu Y, Han J, Zhou R, Wen J (2016) Bayesian networks-based association rules and knowledge reuse in maintenance decision-making of industrial product-service systems. Procedia CIRP 47:198–203. https://doi.org/10.1016/j.procir.2016.03.046
Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Trans Cybern 43(6):1656–1671. https://doi.org/10.1109/TSMCB.2012.2227469
Yang X-S (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley. https://doi.org/10.1002/9780470640425
Yang X-S (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspired Comput 3(5):267–274. https://doi.org/10.1504/IJBIC.2011.042259
Yang R, Xu M, Jones P, Samatova N (2017) Real time utility-based recommendation for revenue optimization via an adaptive online top-k high utility itemsets mining model. In: 2017 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), pp 1859–1866. https://doi.org/10.1109/FSKD.2017.8393050
Yao H, Hamilton HJ, Butz CJ A foundational approach to mining itemset utilities from databases, pp 482–486. https://doi.org/10.1137/1.9781611972740.51. https://epubs.siam.org/doi/abs/10.1137/1.9781611972740.51
Yao H, Hamilton HJ (2006) Mining itemset utilities from transaction databases. Data Knowl Eng 59(3):603–626, including: ER 2003. https://doi.org/10.1016/j.datak.2005.10.004. https://www.sciencedirect.com/science/article/pii/S0169023X05001643
Yeh J-S, Chang C-Y, Wang Y-T (2008) Efficient algorithms for incremental utility mining. In: Proceedings of the 2nd international conference on ubiquitous information management and communication, ICUIMC ’08, Association for Computing Machinery, New York, NY, USA, p 212–217. https://doi.org/10.1145/1352793.1352839
Yeh J-S, Hsu P-C (2010) Hhuif and msicf: novel algorithms for privacy preserving utility mining. Expert Syst Appl 37(7):4779–4786. https://doi.org/10.1016/j.eswa.2009.12.038
Yun U, Kim J (2015) A fast perturbation algorithm using tree structure for privacy preserving utility mining. Expert Syst Appl 42(3):1149–1165. https://doi.org/10.1016/j.eswa.2014.08.037
Zadeh L (1975) The concept of a linguistic variable and its application to approximate reasoning-i. Inf Sci 8(3):199–249. https://doi.org/10.1016/0020-0255(75)90036-5. https://www.sciencedirect.com/science/article/pii/0020025575900365
Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372–390. https://doi.org/10.1109/69.846291
Zhang SW Yudong, Ji G (2013) A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm. Mathematical Prob Eng, article ID 753251. https://doi.org/10.1155/2013/753251. https://www.hindawi.com/journals/mpe/2013/753251/
Zhang SW Yudong, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Prob Eng, article ID 931256. https://doi.org/10.1155/2015/931256
Zhang X, Tian Y, Jin Y (2015) A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 19(6):761–776. https://doi.org/10.1109/TEVC.2014.2378512
Zhang X, Tian Y, Jin Y (2016) Approximate non-dominated sorting for evolutionary many-objective optimization. Inf Sci 369(C):14–33. https://doi.org/10.1016/j.ins.2016.06.007
Zhang X, Duan F, Zhang L, Cheng F, Jin Y, Tang K (2017) Pattern recommendation in task-oriented applications: a multi-objective perspective [application notes]. IEEE Comput Intell Magaz 12(3):43–53. https://doi.org/10.1109/MCI.2017.2708578
Zhang X, Tian Y, Cheng R, Jin Y (2018) A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans Evol Comput 22(1):97–112. https://doi.org/10.1109/TEVC.2016.2600642
Zhang Y, Wu J, Wang H (2019) Neural binary representation learning for large-scale collaborative filtering. IEEE Access 7:60752–60763. https://doi.org/10.1109/ACCESS.2019.2915331
Zhang Q, Fang W, Sun J, Wang Q (2019) Improved genetic algorithm for high-utility itemset mining. IEEE Access 7:176799–176813. https://doi.org/10.1109/ACCESS.2019.2958150
Zhang L, Fu G, Cheng F, Qiu J, Su Y (2018) A multi-objective evolutionary approach for mining frequent and high utility itemsets. Appl Soft Comput 62:974–986. https://doi.org/10.1016/j.asoc.2017.09.033. https://www.sciencedirect.com/science/article/pii/S1568494617305720
Zhang L, Luo P, Chen E, Wang M (2016) Revisiting bound estimation of pattern measures: a generic framework. Inf Sci 339:254–273. https://doi.org/10.1016/j.ins.2015.12.036. https://www.sciencedirect.com/science/article/pii/S0020025516000190
Zheng Z, Kohavi R, Mason L (2001) Real world performance of association rule algorithms. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’01, Association for Computing Machinery, New York, NY, USA, p 401–406. https://doi.org/10.1145/502512.502572
Zida S, Fournier-Viger P, Lin JC-W, Wu C-W, Tseng VS (2015) Efim: a highly efficient algorithm for high-utility itemset mining. In: Sidorov G, Galicia-Haro SN (eds) Advances in artificial intelligence and soft computing. Springer, Cham, pp 530–546
Zille H, Ishibuchi H, Mostaghim S, Nojima Y (2018) A framework for large-scale multiobjective optimization based on problem transformation. IEEE Trans Evol Comput 22(2):260–275. https://doi.org/10.1109/TEVC.2017.2704782
Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Eiben AE, Bäck T, Schoenauer M, Schwefel H-P (eds) Parallel Prob Solv Nature PPSN V. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 292–301
Zitzler E, Laumanns M, Thiele L Spea2: improving the strength pareto evolutionary algorithm, TIK-report 103
Zuo Y, Gong M, Zeng J, Ma L, Jiao L (2015) Personalized recommendation based on evolutionary multi-objective optimization [research frontier]. Comp Intell Mag 10(1):52–62. https://doi.org/10.1109/MCI.2014.2369894
Author information
Authors and Affiliations
Contributions
K.S conceived of the presented idea. R.K. developed the theory and performed the computations. R.K. implemented the example and tables related to the running example. R.K. provided the data for Tables 6, 8, 10 and 11. K.S. provided the data for Tables 7 and 9. Both the authors analyzed and contributed to the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflicts of interest. The article does not contain any studies with human or animal subjects. This article presents a survey of soft computing-based high-utility itemsets mining approaches.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kumar, R., Singh, K. A survey on soft computing-based high-utility itemsets mining. Soft Comput 26, 6347–6392 (2022). https://doi.org/10.1007/s00500-021-06613-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-06613-4