An Efficient Clustering Algorithm Based on Quantum-Behaved Particle Swarm Optimization
K-Means clustering is one of the widely used clustering techniques, however the major drawback of it is that it often gets stuck at local minima and the result is largely dependent on the choice of the initial clustering centers. An efficient clustering algorithm based on Quantum-Behaved Particle Swarm Optimization, called QPSO-clustering, is presented in this article. Three data sets are employed to test the performance of QPSO-clustering. Performance comparison among k-means clustering, PSO-clustering and QPSO-clustering are also provided. The experimental results show that QPSO-clustering provides better performance than PSO-clustering as well as having less parameter to control than PSO-clustering.
K-Means clustering Genetic Algorithms Particle Swarm Optimization Quantum-Behaved Particle Swarm Optimization
Xingye Zhang Wenbo Xu
School of Textiles & Clothing, Southern Yangtze University Wuxi, Jiangsu, China School of Information Technology, Southern Yangtze University Wuxi, Jiangsu, China
国际会议
杭州
英文
603-606
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）