This paper presents an evolutionary particle swarm optimization (PSO) learning-based method to optimally cluster N data points into K clusters. The hybrid PSO and K-means algorithm with a novel alternative metric, called Alternative KPSO-clustering (AKPSO), is developed to automatically detect the cluster centers of geometrical structure data sets. The alternative metric is known has more robust ability than the common-used Euclidean norm. In AKPSO algorithm, the special alternative metric is considered to improve the traditional K-means clustering algorithm to deal with various structure data sets. For testing the performance of the proposed method, this paper will show the experience results by using several artificial and real data sets. Simulation results compared with some well-known clustering methods demonstrate the robustness and efficiency of the novel AKPSO method.
[1] Selim, S. Z. and Ismail, M. A., “K-means Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality,” IEEE Trans. Pattern Anal. Mach. Intell. Vol. 6, pp. 8187 (1984).
[2] Wu, K.-L. and Yang, M.-S., “Alternative C-means Clustering Algorithms,” Pattern Recognition, Vol. 35, pp. 22672278 (2002).
[3] Maulik, U. and Bandyopadhyay, S., “Genetic Algorithm-based Clustering Technique,” Pattern Recognition Vol. 33, pp. 14551465 (2000).
[4] Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proc. of IEEE International Conference on Neural Networks (ICNN), Perth, Australia, Vol. 4, pp. 19421948 (1995).
[5] Eberhart, R. and Kennedy, J., “A New Optimizer Using Particle Swarm Theory,” Proc. 6th Int. Symposium on Micro Machine and Human Science, pp. 3943 (1995).
[6] Eberhart, R. C. and Shi, Y., “Particle Swarm Optimization: Developments, Applications and Resources,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), Seoul, Korea (2001).
[7] Filho, J. L. R., Treleaven, P. C. and Alippi, C., “Genetic Algorithm Programming Environments,” IEEE Comput. Vol. 27, pp. 2843 (1994).
[8] Anderberg, M. R., Cluster Analysis for Application, Academic Press, New York, NY, U.S.A. (1973).
[9] Allen Gersho and Robert M. Gray, Vector Quantization and Signal Compression, Springer Verlag, The Netherlands (1992).
[10] Chen, Ching-Yi. and Ye, Fun., “Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis,” IEEE ICNSC 2004, Taipei, Taiwan, R.O.C., pp. 789794 (2004).
[11] Chen, Ching-Yi. and Ye, Fun., “K-means Algorithm Based on Particle Swarm Optimization,” 2003 International Conference on Informatics, Cybernetics, and Systems, I-Shou University, Taiwan, R.O.C. pp. 1470 1475 (2003).
[12] Bandopadhyay, S. and Maulik, U., “Genetic Clustering for Automatic Evolution of Clusters and Application to Image Classification,” Pattern Recognition, Vol. 35, pp. 11971208 (2002).
[13] Wong, C. C. and Lin, B. C., “Neighbor-based Clustering Algorithm,” International Journal of Electrical Engineering, Vol. 11, pp. 173181 (2004).
We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.
