A Maz-Min Clustering Method for k-Means Algorithm of Data Clustering

摘要：

As it is known that the performance of the k-means algorithm for data clustering largely depends on the choice of the initial centers, and the algorithM generally uses random procedures to get them. In order to improve the efficiency of the k-means algorithm, a good selection method of clustering starting centers is proposed in this paper. The proposed algorithm determines an initial scale for each cluster of patterns, and calculates initial clustering centers according to the norm of the points. Experiments results show that the proposed algorithm provides good performance of clustering.

关键词： Data Mining Nonconvez Optimization Clustering Pattern Recognition

作者: Baolan Yuan Wanjun Zhang Yubo Yuan

作者单位: School of Information Engineering Hangzhou Dianzi University,Hangzhou 310012.P.R. China School of Software Technology Hangzhou Dianzi University, Hangzhou 310012, P.R. China Department of Information and Mathematics Sciences China Jiliang University, Hangzhou 310018, P.R. C

会议类型: 国际会议

会议名称: The First World Congress on Global Optimization in Engineering & Science(第一届工程与科学全局优化国际会议 WCGO2009)

会议地点: 长沙

会议语种:英文

页码: 399-406

在线出版日期: 2009-06-01（万方平台首次上网日期，不代表论文的发表时间）