An Effective Hybrid Method Based on DE, GA, and K-means for Data Clustering

  • Jay Prakash
  • Pramod Kumar Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


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.


Evolutionary algorithm Data clustering Differential algorithm  Genetic algorithm  K-means 


  1. 1.
    Ali, M.M.: Törn, A.: Population set-based global optimization algorithms: Some modifications and numerical studies. Comput. Oper. Res. 31(10), 1703–1725 (2004)Google Scholar
  2. 2.
    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)CrossRefMATHGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Chuang, L.Y., Hsiao, C.J., Yang, C.H.: Chaotic particle swarm optimization for data clustering. Expert Syst. Appl. 38, 14555–14563 (2011)CrossRefGoogle Scholar
  5. 5.
    Cura, T.: A particle swarm optimization approach to clustering. Expert Syst. Appl. 39, 1582–1588 (2012)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms-in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., London (1989)MATHGoogle Scholar
  8. 8.
    Handl, J., Knowles, J.: Improving the scalability of multiobjective clustering. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 2372–2379 (2005)Google Scholar
  9. 9.
    He, H., Tan, Y.: A two-stage genetic algorithm for automatic clustering. Neurocomput. 81, 49–59 (2012)CrossRefGoogle Scholar
  10. 10.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Engle-wood Cliffs, NJ (1988)MATHGoogle Scholar
  11. 11.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Kwedlo, W.: A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn. Lett. 32, 1613–1621 (2011)CrossRefGoogle Scholar
  14. 14.
    Laszlo, M., Mukharjee, S.: A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recogn. Lett. 28, 2359–2366 (2007)CrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    Murphy, P., Aha, D.: UCI repository of machine learning data bases. (1995). URL
  17. 17.
    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)Google Scholar
  18. 18.
    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)CrossRefMATHMathSciNetGoogle Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    Velmurugan, T., Santhanam, T.: A survey of partition based clustering algorithms on data mining: an experimental approach. Int. Technol. J. 10, 478–484 (2011)Google Scholar
  22. 22.
    Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3), 645–678 (2005)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

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

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