Partitional Algorithms for Hard Clustering Using Evolutionary and Swarm Intelligence Methods: A Survey

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)


Evolutionary and swarm intelligence methods attracted attention and gained popularity among the data mining researchers due to their expedient implementation, parallel nature, ability to search global optima and other advantages over conventional techniques. These methods along with their variants and hybrid approaches have emerged as worthwhile class of methods for clustering. Clustering is an unsupervised classification method. The partitional clustering algorithms look for hard clustering; they decompose the dataset into a set of disjoint clusters. This paper describes a brief review of evolutionary and swarm intelligence methods with their variants and hybrid approaches designed for partitional clustering algorithms for hard clustering of datasets.


Evolutionary algorithm Swarm intelligence Clustering Unsupervised classification 


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  1. Fraley, C., Raftery, A. E.: How many clusters? Which clustering method? Answer via model-based cluster analysis. The Computer Jounal. 41, 578–588 (1998).Google Scholar
  2. Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Transaction Neural Networks. vol. 16, no. 3, pp. 645–678 May (2005).Google Scholar
  3. Cura, T.: A Particle Swarm Optimization approach to clustering. Expert Systems with Applications.39,1582-1588(2012).Google Scholar
  4. Jain, A. K., Dubes, R. C. :Algorithms for clustering data. Engle-wood Cliffs, NJ: Prentice-Hall, 1988.Google Scholar
  5. Hansen, P., Jaumard, B.:Cluster analysis and mathematical programming. Mathematical Programming. 79, 191–215 (1997).Google Scholar
  6. Hruschka, E. R., Campello, R. J. G. B., Alex, A. F., de Carvalho, A. C. P. L. F.,:A survey of evolutionary algorithms for clustering” IEEE Transactions on Systems, Man, and Cybernetics—Part C:Applications and Reviews. vol. 39, no. 2, pp. 133–155 March (2009).Google Scholar
  7. Jain, A. K., Murty, M. N., Flynn, P. J.: Data clustering: A review .ACM Comput. Surv., vol. 31, no. 3, pp. 264–323, Sep. (1999).Google Scholar
  8. Xu, R., Xu, J., Wunsch II,D.C: A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Transactions on systems, man, and cybernatics-part B: Cybernetics. vol. 42, no. 4, pp. 1243–1256, Aug.(2012).Google Scholar
  9. Bigus, J. P.: Data mining with neural networks. McGraw-Hill, New York (1996).Google Scholar
  10. Price, K.V., Storn, R.M., Lampinen, J. A.: Differential evolution: A practical approach to global optimization. Berlin: Springer. (2005).Google Scholar
  11. Kennedy, J., Eberhart, R.: Morgan Kaufmann Publishers Inc. San Francisco, CA, USA (2001).Google Scholar
  12. Goldberg, D.E.: Genetic Algorithms-in Search, optimization and machine learning. Addison- Wesley Publishing Company Inc., London (1989).Google Scholar
  13. Ali, M.M., T¨orn, A.: Population set-based global optimization algorithms: Some Modifications and Numerical Studies. Computers & Operations Research. 31(10),1703–1725 (2004).Google Scholar
  14. Velmurugan, T., Santhanam, T.:A Survey of partition basedclustering algorithms on data mining: An experimental approach:,International Technology Journal. 10, 478-484(2011).Google Scholar
  15. Kaufman, L., Rousseeuw, P.J.:Clustering by means of Medoids, in Statistical Data Analysis Based on the L–Norm and Related Methods, edited by Y. Dodge, North-Holland, 405–416(1987).Google Scholar
  16. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In Proceedings of the IEEE International Joint Conference on Neural Networks, 1942–1948 (1995).Google Scholar
  17. Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, applications and resources. In Proceedings of the IEEE Congress on Evolutionary Computation. 1, 27–30, May (2001).Google Scholar
  18. Chuang, L., Hsiao, C., Yang, C.: Chaotic Particle Swarm Optimization for data clustering. Expert Systems with Applications. 38, 14555–14563 (2011).Google Scholar
  19. Kennedy, J., Eberhart, R.C.: A Discrete binary version of the Particle Swarm algorithm. In Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, 4104–4109 (1997).Google Scholar
  20. Kwedlo, W.: A clustering method combining differential evolution with the K-means algorithm. Pattern Recognition Letters. 32, 1613-1621 (2011).Google Scholar
  21. Storn, R., Price, K.: Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. Vol 11, no. 4, 341–359, Dec. (1997).Google Scholar
  22. Tsai, C.-Y., Kao, I.-W.: Particle swarm optimization with selective particle regeneration for data clustering. Expert Systems with Applications .38, 6565–6576 (2011).Google Scholar
  23. Chiou, J-P., Wang, F-S.: A Hybrid method of Differential Evolution with application to optimal control problems of A bioprocess system. In IEEE World Congress on Computational Intelligence, Proceedings of the IEEE International Conference on Evolutionary Computation, 627–632 (1998).Google Scholar
  24. Kuo, R.J., Syu, Y.J., Chen, Zhen-Yao, Tien,F.C.: Integration of Particle Swarm Optimization and Genetic Algorithm for dynamic clustering . Information Sciences.195,124-140 (2012).Google Scholar
  25. Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved Differential Evolution algorithm. IEEE Transactions on System, Man, and Cybernetics-Part A: Systems and Humans, VOL. 38, NO. 1, JAN. (2008).Google Scholar
  26. Raghavan, V.V., Birchand, K.:A clustering strategy based on a formalism of the reproductive process in a natural system,” in Proc. 2nd Int. Conf. Inf. Storage Retrieval. 10–22 (1979).Google Scholar
  27. Kwedlo,W., Iwanowicz, P. : Using Genetic Algorithm for Selection of initial cluster centers for the K-Means method . ICAISC, Part II, LNAI 6114, 165–172 (2010).Google Scholar
  28. Bandyopadhyay, S., Maulik, U.:Genetic clustering for automatic evolution of clusters and application to image classification, Pattern Recognition., vol. 35, no. 6, 1197–1208, Jun.(2002).Google Scholar
  29. Omran, M., Engelbrecht,A., Salman, A.:Dynamic clustering using Particle Swarm Optimization with application in unsupervised image classification.Proceedings of World academy of science, engineering and technology. Vol.9,199-204, Nov. (2005).Google Scholar
  30. Qian, X.-D., Li-Wie.: Date Clustering using principal component analysis and Particle Swarm Optimization. In: Proceedings of the 5th International Conference on Computer Science & Education Hefei, China.493-497 (2010).Google Scholar
  31. Xu, R., Xu, J., Wunsch II,D.C: Clustering with Differential Evolution Particle Swarm Optimization. In: IEEE Congress on Evolutionary Computation CEC (2010).Google Scholar
  32. Das, S., Abraham, A., Konar, A.: Automatic kernel clustering with a multi-elitist Particle Swarm Optimization algorithm. Pattern Recognition Letters 29, 688–699 (2008).Google Scholar
  33. Aloise, D., Deshpande, A., Hansen, P., Popat, P.: NP-hardness of euclidean sum-of-squares clustering. Machine Learning. 75, 245–248 (2009).Google Scholar
  34. Abdel-Kader, R.F.: Genetically Improved PSO algorithm for efficient data clustering. In: Proceedings of the IEEE Second International Conference on Machine Learning and Computing.71-75 (2010).Google Scholar
  35. He, H., Tan, Y.: A Two-stage Genetic Algorithm for automatic clustering. Neurocomputing. 81, 49-59 (2012).Google Scholar
  36. Turi, R.H.: Clustering-based colour image segmentation, PhD Thesis, Monash University, Australia (2001).Google Scholar
  37. Tvrd′ık, J., Kˇriv′y, I.: Differential Evolution with competing strategies Applied to Partitional Clustering. LNCS 7269. 136-144 (2012).Google Scholar
  38. Tian, Y., Liu, D.,Qi,H.: K-Harmonic means data clustering with Differential Evolution. International Conference on Future BioMedical Information Engineering. 369-372(2009).Google Scholar
  39. Chang, D., Zhang, X., Zheng, C., Zhang, D.: A robust dynamic niching Genetic Algorithm with niche migration for automatic clustering problem. Pattern Recognition. 43, 1346–1360 (2010).Google Scholar
  40. Wang, J., Zhang, H., Dong, X., Xu, B., Mei, B.: An effective hybrid crossover operator for Genetic Algorithms to solve K-means clustering problem. Sixth International Conference on Natural Computation (ICNC). 2271-2275(2010).Google Scholar

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© Springer India 2013

Authors and Affiliations

  1. 1.Computational Intelligence and Data Mining Research LabABV-Indian Institute of Information Technology and ManagementGwaliorIndia

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