Abstract
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.
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
Abraham, A., Guo, H., Liu, H.: Swarm Intelligence: Foundations, Perspectives and Applications, Swarm Intelligence in Data Mining. Springer, Germany (2006)
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)
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)
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)
Cowgill, M., Harvey, R., Watson, L.: A genetic algorithm approach to cluster analysis. Comput. Math. Appl. 37, 99–108 (1999)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Nie, F., Tu, T., Pan, M., Rong, Q., Zhou, H.: Advances in Intelligent and Soft Computing, vol. 139, pp. 67–73. Springer, Heidelberg (2012)
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)
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)
Kao, Y., Cheng, K., et al.: An ACO-based clustering algorithm. In: Dorigo, M. (ed.) LNCS, pp. 340–347. Springer, Berlin (2006)
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)
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)
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)
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)
Mirkin, B.: Mathematical Classification and Clustering. Kluwer, Dordrecht (1996)
Paterlini, S., Krink, T.: Differential evolution and particle swarm optimization in partitional clustering. Computational Statistics and Data Analysis 50, 1220–1247 (2006)
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)
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)
Sheng, W., Liu, X.: A genetic it k-medoids clustering algorithm. J. Heuristics 12, 447–466 (2006)
Chen, T.Y., Cheng, Y.L.: Global optimization using hybrid approach. WSEAS Trans. Math. 7(6), 254–262 (2008)
Tseng, L., Yang, S.: A genetic approach to the automatic clustering problem. Pattern Recogn. 34, 415–424 (2001)
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)
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)
Xu, R., Wunsch II, D.: Survey of clustering algorithms. IEEE Trans. Neural Networks 16(3), 645–678 (2005)
Yang, X.S., Deb, S.: Engineering Optimisation by Cuckoo Search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)
Yang, Y., Kamel, M.S.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recogn. 39, 1278–1289 (2006)
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
Manikandan, P., Selvarajan, S. (2014). Data Clustering Using Cuckoo Search Algorithm (CSA). 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_133
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_133
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)