Data Clustering Using Cuckoo Search Algorithm (CSA)

  • P. Manikandan
  • S. Selvarajan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Cluster Analysis is a popular data analysis in data mining technique. Clusters play a vital role for users to organize, summarize and navigate the data effectively. Swarm Intelligence (SI) is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives. SI technique is integrated with clustering algorithms. This paper proposes new approaches for using Cuckoo Search Algorithm (CSA) to cluster data. It is shown how CSA can be used to find the optimally clustering N object into K clusters. The CSA is tested on various data sets, and its performance is compared with those of K-Means, Fuzzy C-Means, Fuzzy PSO and Genetic K-Means clustering. The simulation results show that the new method carries out better results than the K-Means, Fuzzy C-Means, Fuzzy PSO and Genetic K-Means.


Clustering Swarm Intelligence (SI) CSA K-Means Fuzzy C-Means Fuzzy PSO Genetic K-Means 


  1. 1.
    Abraham, A., Guo, H., Liu, H.: Swarm Intelligence: Foundations, Perspectives and Applications, Swarm Intelligence in Data Mining. Springer, Germany (2006)Google Scholar
  2. 2.
    Azzag, H., Venturini, G., Oliver, A., Gu, C.: A hierarchical ant based clustering algorithm and its use in three real-world applications. J. Oper. Res. 179, 906–922 (2007)CrossRefMATHGoogle Scholar
  3. 3.
    Babu, G., Murty, M.: A near-optimal initial seed value selection in k-means algorithm using a genetic algorithm. Pattern Recogn. Lett. 14(10), 763–769 (1993)CrossRefMATHGoogle Scholar
  4. 4.
    Chen, L., Tu, L., Chen, H.: A novel ant clustering algorithm with digraph. In: Wang, L., Chen, K., Ong, Y.S. (eds.) LNCS, pp. 1218–1228. Springer, Berlin (2005)Google Scholar
  5. 5.
    Cowgill, M., Harvey, R., Watson, L.: A genetic algorithm approach to cluster analysis. Comput. Math. Appl. 37, 99–108 (1999)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)MATHGoogle Scholar
  7. 7.
    Nie, F., Tu, T., Pan, M., Rong, Q., Zhou, H.: Advances in Intelligent and Soft Computing, vol. 139, pp. 67–73. Springer, Heidelberg (2012)Google Scholar
  8. 8.
    He, Y., Hui, S.C., Sim, Y., et al.: A novel ant-based clustering approach for document clustering. In: Ng, H.T. (ed.) LNCS, pp. 537–544. Springer, Berlin (2006)Google Scholar
  9. 9.
    Janson, S., Merkle, D., et al.: A new multi-objective particle swarm optimization algorithm using clustering applied to automated docking. In: Blesa, M.J. (ed.) LNCS, pp. 128–141. Springer, Berlin (2005)Google Scholar
  10. 10.
    Kao, Y., Cheng, K., et al.: An ACO-based clustering algorithm. In: Dorigo, M. (ed.) LNCS, pp. 340–347. Springer, Berlin (2006)Google Scholar
  11. 11.
    Ozdamar, L.: A dual sequence simulated annealing algorithm for constrained optimization. In: Proceedings of the 10th WSEAS International Conference on, Applied Mathematics, pp. 557–564 (2006)Google Scholar
  12. 12.
    Liao, S.-H., Wen, C.-H.: Artificial neural networks classification and clustering of methodologies and applications - literature analysis from 1995 to 2005. Expert Syst. Appl. 32, 1–11 (2007)CrossRefGoogle Scholar
  13. 13.
    Verma, M., Srivastava, M., Chack, N., Kumar Diswar, A., Gupta, N.: A comparative study of various clustering algorithms in data mining. Int. J. Eng. Res. Appl. (IJERA) 2 1379–1384 (2012)Google Scholar
  14. 14.
    Meng, L., Wu, Q.H., Yong, Z.Z., et al.: A faster genetic clustering algorithm. In: Cagnoni, S. (ed.) LNCS, pp. 22–33. Springer, Berlin (2000)Google Scholar
  15. 15.
    Mirkin, B.: Mathematical Classification and Clustering. Kluwer, Dordrecht (1996)CrossRefMATHGoogle Scholar
  16. 16.
    Paterlini, S., Krink, T.: Differential evolution and particle swarm optimization in partitional clustering. Computational Statistics and Data Analysis 50, 1220–1247 (2006)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer, New York (2005)CrossRefGoogle Scholar
  18. 18.
    Shen, H.-Y., Peng, X.-Q., Wang, J.-N., Hu, Z.-K.: A mountain clustering based on improved PSO algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds.) LNCS, pp. 477–481. Springer, Berlin (2005)Google Scholar
  19. 19.
    Sheng, W., Liu, X.: A genetic it k-medoids clustering algorithm. J. Heuristics 12, 447–466 (2006)CrossRefGoogle Scholar
  20. 20.
    Chen, T.Y., Cheng, Y.L.: Global optimization using hybrid approach. WSEAS Trans. Math. 7(6), 254–262 (2008)Google Scholar
  21. 21.
    Tseng, L., Yang, S.: A genetic approach to the automatic clustering problem. Pattern Recogn. 34, 415–424 (2001)CrossRefMATHGoogle Scholar
  22. 22.
    Viswanathan, G.M., Raposo, E.P., da Luz, M.G.E.: Lévy flights and superdiffusion in the context of biological encounters and random searches. Phys. Life Rev. 5(3), 133–150 (2008)CrossRefGoogle Scholar
  23. 23.
    Wu, F.-X., Zhang, W.J., Kusalik, A.J.: A genetic k-means clustering algorithm applied to gene expression data. In: Xiang, Y., Chaib-draa, B. (eds.) LNAI, pp. 520–526. Springer, Berlin (2003)Google Scholar
  24. 24.
    Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3), 645–678 (2005)CrossRefGoogle Scholar
  25. 25.
    Yang, X.S., Deb, S.: Engineering Optimisation by Cuckoo Search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)MATHGoogle Scholar
  26. 26.
    Yang, Y., Kamel, M.S.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recogn. 39, 1278–1289 (2006)CrossRefMATHGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Paavaai Group of InstitutionsNamakkalIndia
  2. 2.Muthayammal Technical CampusRasipuramIndia

Personalised recommendations