Abstract
In order to find most centre of the density of the sample set this paper combines MCA and PSO, and presents a mountain clustering based on improved PSO (MCBIPSO) algorithm. A mountain clustering method constructs a mountain function according to the density of the sample, but it is not easy to find all peaks of the mountain function. The improved PSO algorithm is used to find all peaks of the mountain function. The simulation results show that the MCBIPSO algorithm is successful in deciding the density clustering centers of data samples.
This research was supported by National Nature Science Foundation of China (50374079).
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References
Hand, D., Mannila, H.: Padhraic Smyth: Principles of Data Mining. MIT Press, Cambridge (2001)
Jain, A.K., Dubes, R.C.: A1gorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
Xiaoshuai, X., Licheng, J.: Clustering Method in the Field of Data Mining. Journal of Circuits and Systems 8(1) (2003)
Yager, R.R., Filve, D.P.: Generation of fuzzy rules by mountain clustering. Journal of Intelligent and Fuzzy Systems 2, 209–219 (1994)
Yager, R.R., Filve, D.P.: Essential of fuzzy modeling and control. John Wiley &Sons, Inc., Chichester (1994)
Chiu, S.L.: Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy System 2(3) (1994)
Vose, M.D.: The simple genetic algorithms: foundations and theory. MIT Press, Cambridge (1999)
Mingqiang, L., Jishong, K., Dan, L., et al.: Principle and application of the genetic algorithms. Science press, Beijing (2002)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE, Int. Conf. on Neural Networks, Perth, pp. 1942–1948 (1995)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc 6th Int Symposium on Micro Machine and Human Science, Nagoya, pp. 39–43 (1995)
Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Berlin (1999)
Hong, G., Zongyuan, M.: Research on of Parameters Immune Algorithm. Journal of South China University of Technology (Natural Science Edition) 30(12) (December 2002)
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Shen, Hy., Peng, Xq., Wang, Jn., Hu, Zk. (2005). A Mountain Clustering Based on Improved PSO Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_58
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DOI: https://doi.org/10.1007/11539902_58
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