, Volume 17, Issue 2, pp 115–139 | Cite as

Metaheuristics for data mining

Survey and opportunities for big data
  • Clarisse Dhaenens
  • Laetitia JourdanEmail author
Invited Survey


In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan, Metaheuristics for big data, Wiley, New York, 2016); additionally, updated references and an analysis of the current trends are presented.


Metaheuristics Clustering Association rules Classification Feature selection Big data 

Mathematics Subject Classification

90-02 68-02 



  1. Abdul-Rahman S, Bakar AA, Mohamed-Hussein ZA (2013) Optimizing big data in bioinformatics with swarm algorithms. In: 2013 IEEE 16th international conference on computational science and engineering, pp 1091–1095.
  2. Abubaker A, Baharum A, Alrefaei M (2015) Automatic clustering using multi-objective particle swarm and simulated annealing. PLoS ONE 10(7):e0130995Google Scholar
  3. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: VLDB ’94: Proceedings of the 20th international conference on very large data bases. Morgan Kaufmann Publishers Inc, pp 487–499Google Scholar
  4. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data. ACM Press, pp 207–216Google Scholar
  5. Alam S, Dobbie G, Koh YS, Riddle P, Rehman SU (2014) Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol Comput 17:1–13Google Scholar
  6. Alatas B, Akin E, Karci A (2008) Modenar: multi-objective differential evolution algorithm for mining numeric association rules. Appl Soft Comput 8(1):646–656Google Scholar
  7. Alba E, García-Nieto J, Jourdan L, Talbi, EG (2007) Gene selection in cancer classification using pso/svm and ga/svm hybrid algorithms. In: IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 284–290Google Scholar
  8. Anand R, Vaid A, Singh PK (2009) Association rule mining using multi-objective evolutionary algorithms: strengths and challenges. In: World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 385–390Google Scholar
  9. Baalamurugan K, Bhanu S (2018) An efficient clustering scheme for cloud computing problems using metaheuristic algorithms. S.V. Cluster Comput, pp 1–11Google Scholar
  10. Bacardit J, Butz MV (2007) Data mining in learning classifier systems: comparing \(XCS\) with \(GAssist\). Learn Classif Syst 4399:282–290Google Scholar
  11. Bala J, Huang, J, Vafaie H, DeJong K, Wechsler H. (1995) Hybrid learning using genetic algorithms and decision trees for pattern classification. In: IJCAI, vol 1, pp 719–724Google Scholar
  12. Bandyopadhyay S, Maulik U (2001) Nonparametric genetic clustering: comparison of validity indices. IEEE Trans Syst Man Cybern Part C Appl Rev 31(1):120–125. Google Scholar
  13. Bandyopadhyay S, Mukhopadhyay A, Maulik U (2007) An improved algorithm for clustering gene expression data. Bioinformatics 23(21):2859–2865Google Scholar
  14. Barba-Gonzaléz C, García-Nieto J, Nebro AJ, Aldana-Montes JF (2017) Multi-objective big data optimization with jmetal and spark. In: International conference on evolutionary multi-criterion optimization. Springer, pp 16–30Google Scholar
  15. Barros RC, Basgalupp MP, de Carvalho AC, Freitas AA (2012) A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms. In: Proceedings of the 14th annual conference on genetic and evolutionary computation. ACM, pp 1237–1244Google Scholar
  16. Basgalupp MP, Barros RC, Podgorelec V (2015) Evolving decision-tree induction algorithms with a multi-objective hyper-heuristic. In: Proceedings of the 30th annual ACM symposium on applied computing. ACM, pp 110–117Google Scholar
  17. Begum S, Chakraborty S, Banerjee A, Das S, Sarkar R, Chakraborty D (2018) Gene selection for diagnosis of cancer in microarray data using memetic algorithm. In: Intelligent engineering informatics. Springer, pp 441–449Google Scholar
  18. Bezdek JC, Boggavarapu S, Hall LO, Bensaid A (1994) Genetic algorithm guided clustering. In: International conference on evolutionary computation, pp 34–39Google Scholar
  19. Bong CW, Rajeswari M (2011) Multi-objective nature-inspired clustering and classification techniques for image segmentation. Appl Soft Comput J 11(4):3271–3282. Google Scholar
  20. Borges HB, Nievola JC (2005) Attribute selection methods comparison for classification of diffuse large b-cell lymphoma. In: Fourth international conference on machine learning and applications, 2005. Proceedings. IEEE, pp 6Google Scholar
  21. Boryczka U, Kozak J (2010) Ant colony decision trees—a new method for constructing decision trees based on ant colony optimization. In: Computational collective intelligence technologies and applications. Springer, pp 373–382Google Scholar
  22. Boryczka U, Kozak J (2015) Enhancing the effectiveness of ant colony decision tree algorithms by co-learning. Appl Soft Comput 30:166–178Google Scholar
  23. Bursa M, Lhotska L, Macas M (2007) Hybridized swarm metaheuristics for evolutionary random forest generation. In: 7th international conference on hybrid intelligent systems, 2007. HIS 2007, pp 150–155.
  24. Can U, Alatas B (2017) Automatic mining of quantitative association rules with gravitational search algorithm. Int J Softw Eng Knowl Eng 27(03):343–372Google Scholar
  25. Cano A, Luna JM, Ventura S (2013) High performance evaluation of evolutionary-mined association rules on gpus. J Supercomput 66(3):1438–1461Google Scholar
  26. Che D, Safran M, Peng Z (2013) From big data to big data mining: challenges, issues, and opportunities. In: Database systems for advanced applications. Springer, pp 1–15Google Scholar
  27. Corne D, Dhaenens C, Jourdan L (2012) Synergies between operations research and data mining: the emerging use of multi-objective approaches. Eur J Oper Res 221(3):469–479Google Scholar
  28. Cowgill M, Harvey R, Watson L (1999) Genetic algorithm approach to cluster analysis. Comput Math Appl 37(7):99–108. Google Scholar
  29. Dankolo MN, Radzi NHM, Sallehuddin R, Mustaffa NH (2017) A study of metaheuristic algorithms for high dimensional feature selection on microarray data. In: AIP conference proceedings, vol. 1905. AIP Publishing, p 040010Google Scholar
  30. de la Iglesia B, Reynolds A, Rayward-Smith VJ (2005) Developments on a multi-objective metaheuristic (momh) algorithm for finding interesting sets of classification rules. In: Evolutionary multi-criterion optimization. Springer, pp 826–840Google Scholar
  31. de la Iglesia B, Richards G, Philpott MS, Rayward-Smith VJ (2006) The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification. Eur J Oper Res 169(3):898–917Google Scholar
  32. del Jesus MJ, Gamez JA, Gonzalez P, Puerta JM (2011) On the discovery of association rules by means of evolutionary algorithms. Wiley Interdiscip Rev Data Min Knowl Discov 1(5):397–415Google Scholar
  33. Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113. Google Scholar
  34. Defays D (1977) An efficient algorithm for a complete link method. Comput J 20(4):364–366. Google Scholar
  35. Dhaenens C, Jourdan L (2016) Metaheuristics for big data. Wiley, New YorkGoogle Scholar
  36. Diao R, Shen Q (2015) Nature inspired feature selection meta-heuristics. Artif Intell Rev 44(3):311–340. Google Scholar
  37. Djenouri Y, Bendjoudi A, Mehdi M, Nouali-Taboudjemat N, Habbas Z (2015) Gpu-based bees swarm optimization for association rules mining. J Supercomput 71(4):1318–1344Google Scholar
  38. Djenouri Y, Djenouri D, Habbas Z, Belhadi A (2018) How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem. Distrib Parallel Databases 36(2):369–397Google Scholar
  39. Djenouri Y, Drias H, Habbas Z (2014) Bees swarm optimisation using multiple strategies for association rule mining. Int J Bio-Inspired Comput 6(4):239–249Google Scholar
  40. Dussaut JS, Vidal PJ, Ponzoni I, Olivera AC (2018) Comparing multiobjective evolutionary algorithms for cancer data microarray feature selection. In: 2018 IEEE congress on evolutionary computation (CEC), pp 1–8.
  41. Ebrahimpour MK, Nezamabadi-Pour H, Eftekhari M (2018) Ccfs: a cooperating coevolution technique for large scale feature selection on microarray datasets. Comput Biol Chem 73:171–178Google Scholar
  42. Fahad A, Alshatri N, Tari Z, Alamri A, Khalil I, Zomaya AY, Foufou S, Bouras A (2014) A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans Emerg Top Comput 2(3):267–279. Google Scholar
  43. Fong S, Wong R, Vasilakos AV (2016) Accelerated pso swarm search feature selection for data stream mining big data. IEEE Trans Serv Comput 9(1):33–45. Google Scholar
  44. Freitas AA (2008) A review of evolutionary algorithms for data mining. In: Soft computing for knowledge discovery and data mining. Springer, pp 79–111Google Scholar
  45. Freitas AA (2013) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinGoogle Scholar
  46. Friedrichs F, Igel C (2005) Evolutionary tuning of multiple SVM parameters. Neurocomputing 64:107–117Google Scholar
  47. Gao W (2016) Improved ant colony clustering algorithm and its performance study. In: Computational intelligence and neuroscience.
  48. García-Nieto J, Alba E, Jourdan L, Talbi EG (2009) Sensitivity and specificity based multiobjective approach for feature selection: application to cancer diagnosis. Inf Process Lett 109:887–896Google Scholar
  49. García Piquer Á (2012) Facing-up challenges of multiobjective clustering based on evolutionary algorithms: representations, scalability and retrieval solutions. Ph.D. thesis, Universitat Ramon LlullGoogle Scholar
  50. Gheraibia Y, Moussaoui A, Djenouri Y, Kabir S, Yin PY (2016) Penguins search optimisation algorithm for association rules mining. J Comput Inf Technol 24(2):165–179Google Scholar
  51. Ghosh A, Halder A, Kothari M, Ghosh S (2008) Aggregation pheromone density based data clustering. Inf Sci 178(13):2816–2831. Google Scholar
  52. Ghosh A, Nath B (2004) Multi-objective rule mining using genetic algorithms. Inf Sci 163(1):123–133Google Scholar
  53. Green RC, Wang L, Alam M (2012) Training neural networks using central force optimization and particle swarm optimization: insights and comparisons. Expert Syst Appl 39(1):555–563Google Scholar
  54. Gu S, Cheng R, Jin Y (2018) Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput 22(3):811–822. Google Scholar
  55. Gupta GP, Jha S (2018) Integrated clustering and routing protocol for wireless sensor networks using cuckoo and harmony search based metaheuristic techniques. Eng Appl Artif Intell 68:101–109. Google Scholar
  56. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  57. Han J (2005) Data mining: concepts and techniques. Morgan Kaufmann Publishers Inc., San FranciscoGoogle Scholar
  58. Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1–7. Google Scholar
  59. Handl J, Knowles J (2004) Evolutionary multiobjective clustering. In: Proceedings of the eighth international conference on parallel problem solving from nature. Springer, pp 1081–1091Google Scholar
  60. Handl J, Knowles JD (2007) An evolutionary approach to multiobjective clustering. IEEE Trans Evol Comput 11(1):56–76Google Scholar
  61. Handl J, Knowles J (2012) Clustering criteria in multiobjective data clustering. In: Coello C, Cutello V, Deb K, Forrest S, Nicosia G, Pavone M (eds) Parallel problem solving from nature—PPSN XII, vol 7492. Lecture notes in computer science. Springer, Berlin, pp 32–41Google Scholar
  62. Handl J, Meyer B (2007) Ant-based and swarm-based clustering. Swarm Intell 1(2):95–113Google Scholar
  63. Handl J, Knowles J, Kell D (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15):3201–3212Google Scholar
  64. Heraguemi KE, Kamel N, Drias H (2016) Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies. Appl Intell 45(4):1021–1033Google Scholar
  65. Hilderman R, Hamilton HJ (2013) Knowledge discovery and measures of interest, vol 638. Springer, BerlinGoogle Scholar
  66. Holden N, Freitas AA (2005) A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. In: SIS, pp 100–107Google Scholar
  67. Holden N, Freitas AA (2008) A hybrid pso/aco algorithm for discovering classification rules in data mining. J Artif Evol Appl 2008:2:1–2:11. Google Scholar
  68. Hruschka E, Campello R, Freitas A, de Carvalho A (2009) A survey of evolutionary algorithms for clustering. IEEE Trans Syst Man Cybern Part C Appl Rev 39(2):133–155. Google Scholar
  69. Hu J, Yang-Li X (2007) Association rules mining using multi-objective coevolutionary algorithm. In: International conference on computational intelligence and security workshops, 2007. CISW 2007. IEEE, pp 405–408Google Scholar
  70. Huang DS, Du JX (2008) A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans Neural Netw 19(12):2099–2115Google Scholar
  71. Igel C, Wiegand S, Friedrichs F (2005) Evolutionary optimization of neural systems: the use of strategy adaptation. In: Trends and applications in constructive approximation. Springer, pp 103–123Google Scholar
  72. Jacques J, Taillard J, Delerue D, Jourdan L, Dhaenens C (2013) The benefits of using multi-objectivization for mining pittsburgh partial classification rules in imbalanced and discrete data. In: Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, pp 543–550Google Scholar
  73. Jacques J, Taillard J, Delerue D, Dhaenens C, Jourdan L (2015) Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets. Appl Soft Comput 34:705–720Google Scholar
  74. José-García A, Gómez-Flores W (2016) Automatic clustering using nature-inspired metaheuristics: A survey. Appl Soft Comput J 41:192–213. Google Scholar
  75. Juliusdottir T, Corne D, Keedwell E, Narayanan A (2005) Two-phase ea/k-nn for feature selection and classification in cancer microarray datasets. In: Proceedings of the 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2005, Embassy Suites Hotel La Jolla, La Jolla, CA, USA, November 14 & 15, 2005. IEEE, pp 1–8Google Scholar
  76. Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley series in probability and statistics. Wiley, New York. Google Scholar
  77. Kaufman L, Rousseeuw PJ (2008) Partitioning around medoids (Program PAM), chap 2. Wiley-Blackwell, New York, pp 68–125. Google Scholar
  78. Kaya M (2006) Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules. Soft Comput 10(7):578–586Google Scholar
  79. Kaya M, Alhajj R (2005) Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst 152(3):587–601Google Scholar
  80. Kazmi S, Javaid N, Mughal MJ, Akbar M, Ahmed SH, Alrajeh N (2017) Towards optimization of metaheuristic algorithms for iot enabled smart homes targeting balanced demand and supply of energy. IEEE AccessGoogle Scholar
  81. Khabzaoui M, Dhaenens C, Talbi EG (2004) A multicriteria genetic algorithm to analyze microarray data. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol 2, pp 1874–1881Google Scholar
  82. Khabzaoui M, Dhaenens C, Talbi EG (2005) Parallel genetic algorithms for multi-objective rule mining. In: The 6th MIC2005Google Scholar
  83. Khabzaoui M, Dhaenens C, Talbi EG (2008) Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery. RAIRO-Oper Res Recherche Opérationnelle 42(1):69–83Google Scholar
  84. Khan K, Sahai A (2012) A comparison of ba, ga, pso, bp and lm for training feed forward neural networks in e-learning context. Int J Intell Syst Appl 4(7):23Google Scholar
  85. Kim Y, Street W, Menczer F (2002) Data mining: opportunities and challenges. Feature selection in data mining. Idea Group, Hershey, pp 80–105Google Scholar
  86. Kira K, Rendell LA (1992) A practical approach to feature selection. In: Proceedings of the ninth international workshop on machine learning, pp 249–256Google Scholar
  87. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1):273–324Google Scholar
  88. Krishna K, Murty M (1999) Genetic k-means algorithm. IEEE Trans Syst Man Cybern Part B Cybern 29(3):433–439. Google Scholar
  89. Kurada RR, Pavan DKK, Rao DA (2013) A preliminary survey on optimized multiobjective metaheuristic methods for data clustering using evolutionary approaches. arXiv preprint arXiv:1312.2366
  90. Laney D (2001) 3D data management: controlling data volume, velocity and variety. Gartner. Retrieved 6Google Scholar
  91. Larose DT (2014) Discovering knowledge in data: an introduction to data mining. Wiley, New YorkGoogle Scholar
  92. Leung S, Tang Y, Wong WK (2012) A hybrid particle swarm optimization and its application in neural networks. Expert Syst Appl 39(1):395–405Google Scholar
  93. Lixiang Li MW, Xiao J, Wang C, Yang Y (2012) Data clustering using bacterial foraging optimization. J Intell Inf Syst 38(2):321–341Google Scholar
  94. Liu H, Motoda H (2007) Computational methods of feature selection. Chapman & Hall/Crc data mining and knowledge discovery series. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  95. Ma BB, Fong S, Millham R (2018) Data stream mining in fog computing environment with feature selection using ensemble of swarm search algorithms. In: 2018 conference on information communications technology and society (ICTAS). IEEE, pp 1–6Google Scholar
  96. Maimon O, Rokach L (2007) Soft computing for knowledge discovery and data mining. Springer, BerlinGoogle Scholar
  97. Maimon O, Rokach L (2010) Data mining and knowledge discovery handbook, 2nd edn. Springer, BerlinGoogle Scholar
  98. Manikandan R, Kalpana A (2017) Feature selection using fish swarm optimization in big data. Cluster Comput, pp 1–13Google Scholar
  99. Marinakis Y, Marinaki M, Doumpos M, Matsatsinis N, Zopounidis C (2008) Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment. J Global Optim 42(2):279–293. Google Scholar
  100. Matthews SG, Gongora MA, Hopgood AA (2011) Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm. In: Hybrid artificial intelligent systems. Springer, pp 198–205Google Scholar
  101. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recognit 33(9):1455–1465Google Scholar
  102. McQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp 281–297Google Scholar
  103. Meisel S, Mattfeld D (2010) Synergies of operations research and data mining. Eur J Oper Res 206(1):1–10Google Scholar
  104. Mlakar U, Zorman M, Fister I Jr, Fister I (2017) Modified binary cuckoo search for association rule mining. J Intell Fuzzy Syst 32(6):4319–4330Google Scholar
  105. Mukhopadhyay A, Maulik U (2011) A multiobjective approach to MR brain image segmentation. Appl Soft Comput 11(1):872–880. Google Scholar
  106. Mukhopadhyay A, Maulik U, Bandyopadhyay S (2009) Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes. IEEE Trans Evol Comput 13(5):991–1005Google Scholar
  107. Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello C et al (2014) Survey of multiobjective evolutionary algorithms for data mining: Part II. IEEE Trans Evol Comput 18(1):20–35Google Scholar
  108. Mukhopadhyay A, Maulik U, Bandyopadhyay S (2015) A survey of multiobjective evolutionary clustering. ACM Comput Surv 47(4):61. Google Scholar
  109. Murthy C, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recogn Lett 17(8):825–832. Google Scholar
  110. Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18. Google Scholar
  111. Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 26(9):917–922. Google Scholar
  112. Nunez SG, Attoh-Okine N (2014) Metaheuristics in big data: an approach to railway engineering. In: 2014 IEEE International Conference on Big Data (Big Data). IEEE, pp 42–47Google Scholar
  113. Olafsson S, Li X, Wu S (2008) Operations research and data mining. Eur J Oper Res 187(3):1429–1448Google Scholar
  114. Otero FE, Freitas AA, Johnson CG (2012) Inducing decision trees with an ant colony optimization algorithm. Appl Soft Comput 12(11):3615–3626Google Scholar
  115. Ozbakir L, Turna F (2017) Clustering performance comparison of new generation meta-heuristic algorithms. Knowl Based Syst 130:1–16. Google Scholar
  116. Pandove D, Goel S, Rani R (2018) Systematic review of clustering high-dimensional and large datasets. ACM Trans Knowl Discov Data 12(2):16:1–16:68. Google Scholar
  117. Qodmanan HR, Nasiri M, Minaei-Bidgoli B (2011) Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Syst Appl 38(1):288–298Google Scholar
  118. Rana S, Jasola S, Kumar R (2011) A review on particle swarm optimization algorithms and their applications to data clustering. Artif Intell Rev 35(3):211–222Google Scholar
  119. Rebentrost P, Mohseni M, Lloyd S (2013) Quantum support vector machine for big feature and big data classification. arXiv preprint arXiv:1307.0471
  120. Refaeilzadeh P, Tang L, Liu H (2009) Cross-validation. In: Encyclopedia of database systems. Springer, pp 532–538Google Scholar
  121. Salama KM, Otero FE (2014) Learning multi-tree classification models with ant colony optimization. In: Proceedings international conference on evolutionary computation theory and applications (ECTA-14), pp 38–48Google Scholar
  122. Salama KM, Abdelbar AM, Otero FE (2015) Investigating evaluation measures in ant colony algorithms for learning decision tree classifiers. In: 2015 IEEE symposium series on computational intelligenceGoogle Scholar
  123. Salleb-Aouissi A, Vrain C, Nortet C (2007) Quantminer: a genetic algorithm for mining quantitative association rules. In: IJCAI, vol 7Google Scholar
  124. Sarkar M, Yegnanarayana B, Khemani D (1997) A clustering algorithm using an evolutionary programming-based approach. Pattern Recogn Lett 18(10):975–986Google Scholar
  125. Sawhney R, Mathur P, Shankar R (2018) A firefly algorithm based wrapper-penalty feature selection method for cancer diagnosis. In: Gervasi O, Murgante B, Misra S, Stankova E, Torre CM, Rocha AMA, Taniar D, Apduhan BO, Tarantino E, Ryu Y (eds) Computational science and its applications—ICCSA 2018. Springer, Cham, pp 438–449Google Scholar
  126. Selvi RS, Valarmathi ML (2017) An improved firefly heuristics for efficient feature selection and its application in big data. Biomed Res 28:S236–S241Google Scholar
  127. Shah SC, Kusiak A (2004) Data mining and genetic algorithm based gene/snp selection. Artif Intell Med 31(3):183–196Google Scholar
  128. Sheikh RH, Raghuwanshi MM, Jaiswal AN (2008) Genetic algorithm based clustering: a survey. In: First international conference on emerging trends in engineering and technology. IEEE, pp 314–319Google Scholar
  129. Shelokar P, Jayaraman V, Kulkarni B (2004) An ant colony approach for clustering. Anal Chim Acta 509(2):187–195. Google Scholar
  130. Shenoy PD, Srinivasa K, Venugopal K, Patnaik LM (2003) Evolutionary approach for mining association rules on dynamic databases. In: Advances in knowledge discovery and data mining. Springer, pp 325–336Google Scholar
  131. Shenoy PD, Srinivasa K, Venugopal K, Patnaik LM (2005) Dynamic association rule mining using genetic algorithms. Intell Data Anal 9(5):439–453Google Scholar
  132. Shi SY, Suganthan PN, Deb K (2004) Multiclass protein fold recognition using multiobjective evolutionary algorithms. In: Proceedings of the 2004 IEEE symposium on computational intelligence in bioinformatics and computational biology, 2004. CIBCB’04. IEEE, pp 61–66Google Scholar
  133. Shvachko K, Kuang H, Radia S, Chansler R (2010) The Hadoop distributed file system. In: Proceedings of the 2010 IEEE 26th symposium on mass storage systems and technologies (MSST), MSST ’10, pp. 1–10. IEEE Computer Society, Washington, DC, USA.
  134. Sibson R (1973) Slink: an optimally efficient algorithm for the single-link cluster method. Comput J 16(1):30–34. Google Scholar
  135. Siedlecki W, Sklansky J (1989) A note on genetic algorithms for large-scale feature selection. Pattern Recogn Lett 10(5):335–347. Google Scholar
  136. Sklansky J, Vriesenga M (1996) Genetic selection and neural modeling of piecewise-linear classifiers. Int J Pattern Recogn Artif Intell 10(05):587–612Google Scholar
  137. Song A, Song J, Ding X, Xu G, Chen J (2017) Utilizing bat algorithm to optimize membership functions for fuzzy association rules mining. In: International conference on database and expert systems applications. Springer, pp 496–504Google Scholar
  138. Sörensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22(1):3–18Google Scholar
  139. Suthaharan S (2015) Machine learning models and algorithms for big data classification: thinking with examples for effective learning, vol 36. Springer, BerlinGoogle Scholar
  140. Suttorp T, Igel C (2006) Multi-objective optimization of support vector machines. In: Multi-objective machine learning. Springer, pp 199–220Google Scholar
  141. Tang R, Fong S (2018) Clustering big IoT data by metaheuristic optimized mini-batch and parallel partition-based dgc in hadoop. Future Gener Comput Syst 86:1395–1412Google Scholar
  142. Triguero I, Peralta D, Bacardit J, García S, Herrera F (2015) Mrpr: a mapreduce solution for prototype reduction in big data classification. Neurocomputing 150:331–345Google Scholar
  143. Tsai CW, Chiang MC, Ksentini A, Chen M (2016) Metaheuristic algorithms for healthcare: open issues and challenges. Comput Electr Eng 53:421–434. Google Scholar
  144. Tsai CW, Liu SJ, Wang YC (2017) A parallel metaheuristic data clustering framework for cloud. J Parallel Distrib Comput 116:39–49. Google Scholar
  145. Tseng L, Yang S (2001) Genetic approach to the automatic clustering problem. Pattern Recognit 34(2):415–424. Google Scholar
  146. Xu X, Chen L, Chen Y (2004) A4c: an adaptive artificial ants clustering algorithm. In: Proceedings of the 2004 IEEE symposium on computational intelligence in bioinformatics and computational biology, 2004. CIBCB ’04, pp 268–275.
  147. 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. Google Scholar
  148. Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626. Google Scholar
  149. Yan X, Zhang C, Zhang S (2009) Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst Appl 36(2):3066–3076Google Scholar
  150. Yang CS, Chuang LY, Chen YJ, Yang CH (2008) Feature selection using memetic algorithms. In: Third international conference on convergence and hybrid information technology, 2008. ICCIT’08, vol 1. IEEE, pp 416–423Google Scholar
  151. Zheng Y, Jia L, Cao L (2012) Multi-objective gene expression programming for clustering. Inf Technol Control 41(3):283–294. Google Scholar
  152. Zhang Y, Gong Dw, Cheng J (2017) Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans Comput Biol Bioinform 14(1):64–75. Google Scholar
  153. Zheng B, Zhang J, Yoon SW, Lam SS, Khasawneh M, Poranki S (2015) Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Syst Appl 42(20):7110–7120Google Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CRIStAL - Centre de Recherche en Informatique Signal et Automatique de LilleUniv. Lille, CNRS, Centrale Lille, UMR 9189LilleFrance

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