Abstract
Clustering is an unsupervised classification method and plays essential role in applications in diverse fields. The evolutionary methods attracted attention and gained popularity among the data mining researchers for clustering due to their expedient implementation, parallel nature, ability to search global optima, and other advantages over conventional methods. However, conventional clustering methods, e.g., K-means, are computationally efficient and widely used local search methods. Therefore, many researchers paid attention to hybrid algorithms. However, most of the algorithms lag in proper balancing of exploration and exploitation of solutions in the search space. In this work, the authors propose a hybrid method DKGK. It uses DE to diversify candidate solutions in the search space. The obtained solutions are refined by K-means. Further, GA with heuristic crossover operator is applied for fast convergence of solutions and the obtained solutions are further refined by K-means. This is why proposed method is called DKGK. Performance of the proposed method is compared to that of Deferential Evolution (DE), genetic algorithm (GA), a hybrid of DE and K-means (DEKM), and a hybrid of GA and K-Means (GAKM) based on the sum of intra-cluster distances. The results obtained on three real and two synthetic datasets are very encouraging as the proposed method DKGK outperforms all the competing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, M.M.: Törn, A.: Population set-based global optimization algorithms: Some modifications and numerical studies. Comput. Oper. Res. 31(10), 1703–1725 (2004)
Chang, D., Zhang, X., Zheng, C., Zhang, D.: A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem. Pattern Recogn. 43, 1346–1360 (2010)
Chiou, J.-P., Wang, F.-S.: A hybrid method of di?erential 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, pp. 627–632. (1998)
Chuang, L.Y., Hsiao, C.J., Yang, C.H.: Chaotic particle swarm optimization for data clustering. Expert Syst. Appl. 38, 14555–14563 (2011)
Cura, T.: A particle swarm optimization approach to clustering. Expert Syst. Appl. 39, 1582–1588 (2012)
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(1), 218–237 (2008)
Goldberg, D.E.: Genetic Algorithms-in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., London (1989)
Handl, J., Knowles, J.: Improving the scalability of multiobjective clustering. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 2372–2379 (2005)
He, H., Tan, Y.: A two-stage genetic algorithm for automatic clustering. Neurocomput. 81, 49–59 (2012)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Engle-wood Cliffs, NJ (1988)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Kwedlo, W., Iwanowicz, P.: Using genetic algorithm for selection of initial cluster centers for the K-Means method. In: Proceedings of \(10^{th}\) International Conference on Artificial Intelligence and Soft Computing. Part II, LNAI 6114, pp. 165–172, (2010)
Kwedlo, W.: A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn. Lett. 32, 1613–1621 (2011)
Laszlo, M., Mukharjee, S.: A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recogn. Lett. 28, 2359–2366 (2007)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, 281–297, (1967)
Murphy, P., Aha, D.: UCI repository of machine learning data bases. (1995). URL http://www.sgi.com/tech/mlc/db
Peltokangas, R., Sorsa, A.: Real-coded genetic algorithms and nonlinear parameter identification. University of Oulu Control Engineering Laboratory Report, vol. 34, pp. 1–32 (2008)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Tian, Y., Liu, D., Qi, H.: K-harmonic means data clustering with differential evolution. In: Proceedings International Conference on Future BioMedical Information, Engineering, pp. 369–372, (2009)
Tvrd’ık, J., Křiv’y, I.: Differential evolution with competing strategies applied to partitional clustering. In: Proceedings Symposium on Swarm Intelligence and Differential Intelligence, LNCS 7269. pp. 136–144 (2012)
Velmurugan, T., Santhanam, T.: A survey of partition based clustering algorithms on data mining: an experimental approach. Int. Technol. J. 10, 478–484 (2011)
Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3), 645–678 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Prakash, J., Singh, P.K. (2014). An Effective Hybrid Method Based on DE, GA, and K-means for Data Clustering. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_155
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_155
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)