Quantum-Behaved particle swarm optimization clustering algorithm

Authors:
Jun Sun;Wenbo Xu;Bin Ye
Affiliations:
Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

Quantum-behaved Particle Swarm Optimization (QPSO) is a novel optimization algorithm proposed in the previous work. Compared to the original Particle Swarm Optimization (PSO), QPSO is global convergent, while the PSO is not. This paper focuses on exploring the applicability of the QPSO to data clustering. Firstly, we introduce the K-means clustering algorithm and the concepts of PSO and QPSO. Then we present how to use the QPSO to cluster data vectors. After that, experiments are implemented to compare the performance of various clustering algorithms. The results show that the QPSO can generate good results in clustering data vectors with tolerable time consumption.