Abstract
This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization, we suggest further improvements. Moreover, we gathered standard benchmark datasets and compared our new approach against the standard K-means algorithm, obtaining promising results. Our hybrid mechanism outperforms earlier PSO-based approaches by using simplistic communication between agents.
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
Preview
Unable to display preview. Download preview PDF.
References
Han, F., Kamber, K.: Data mining: Concepts and techniques, pp. 334–395. Academic Press, San Francisco (2001)
Tazutov, A., Kurenkov, N.: Neural network data clustering on the basis of scale invariant entropy. In: International Joint Conference on Neural Networks, pp. 4912–4918 (2006)
Chandra, S., Murthy, J.: An Efficient Hybrid Algorithm for Data Clustering Using Improved Genetic Algorithm and Nelder Mead Simplex Search. In: Proc. of the Int. Conf. on Comp. Int. and Mult. Appl., vol. 1, pp. 498–510 (2007)
Goncalves, M., Andrade, M.: Data Clustering using Self-Organizing Maps segmented by Mathematic Morphology and Simplified Cluster Validity Indexes: an application in remotely sensed images. In: International Joint Conference on Neural Networks, pp. 4421–4428 (2006)
Kennedy, J., Ebenhart, R.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE Int. Conf. on Neural Networks, pp. 1942–1948 (1995)
Passino, K.: Biomimicry of bacterial foraging for distributed optimization and control. Control Systems Magazine, 52–67 (2002)
Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. Wiley, Chichester (2005)
Heylighen, F., Joslyn, C.: Cybernetics and second-order cybernetics. Encyclopedia of Physical Science and Technology. Academic Press, New York (2001)
Bergh, F.: An analysis of particle swarm optimizers. PhD Thesis, University of Pretoria, South Africa (2002)
Clerc, M., Kennedy, J.: The particle swarm: Explosion, stability and convergence. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
van der Merwe, D., Engelbrecht, A.: Data Clustering using particle swarm optimization. In: The 2003 congress on Evolutionary Computation, vol. 1, pp. 215–220 (2003)
Biswas, A., Dasgupta, S.: Synergy of PSO and bacterial foraging optimization: A comprehensive study on. Advances in Soft Computing Series, pp. 255–263. Springer, Heidelberg (2007)
Abraham, A., Roy, S.: Swarm intelligence algorithms for data clustering. Chp. 12, 279–313 (2008)
Asuncion, A., Newman, D.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Theodoris, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olesen, J.R., Cordero H., J., Zeng, Y. (2009). Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2009. Lecture Notes in Computer Science(), vol 5680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03603-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-03603-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03602-6
Online ISBN: 978-3-642-03603-3
eBook Packages: Computer ScienceComputer Science (R0)