A mountain clustering based on improved PSO algorithm

Authors:
Hong-yuan Shen;Xiao-qi Peng;Jun-nian Wang;Zhi-kun Hu
Affiliations:
Institute of Energy and Power Engineering, Central South University, Changsha, China;Institute of Energy and Power Engineering, Central South University, Changsha, China;Institute of Information and Electrical Engineering, Hunan University of Science and technology, Xiangtan, China;Institute of Information Science and Engineering, Central South University, Changsha, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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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.