Study on Application of Improved Particle Swarm Optimization and K-means Algorithm in Clustering Analysis

摘要：

In this paper, Particle Swarm Optimization is improved directing at optimizing parameters of Particle Swarm Optimization, and in other words it is to carry out reasonable regulation to inertia factor and acceleration factor, and carry out crude data clustering by using its effective global search feature to get an initial value which is effective to clustering analysis, and then process clustering analysis by using K-means algorithm. The experimental results show that clustering effect has been enhanced to a great extent.

关键词： Particle Swarm Optimization Parameter Clustering Analysis K-means

作者: Lijuan Zhou Hui Zheng

作者单位: Institute of Information Spreading Engineering Changchun University of Technology Changchun, 130012, Jilin city branch China Telecom Corporation Limited Company Jilin,China

会议类型: 国际会议

会议名称: 2010 Second Asia-Pacific Conference on Information Processing(2010年第二届亚太地区信息处理国际会议 APCIP 2010)

会议地点: 南昌

会议语种:英文

页码: 154-157

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）