文献类型：期刊文献

英文题名：A MAX-MIN CLUSTERING METHOD FOR k-MEANS ALGORITHM OF DATA CLUSTERING

作者：Yuan, Baolan[2];Zhang, Wanjun[3];Yuan, Yubo[1]

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Hangzhou Dianzi Univ, Sch Informat Engn, Hangzhou 310012, Zhejiang, Peoples R China;[3]Hangzhou Dianzi Univ, Sch Software Engn, Hangzhou 310012, Zhejiang, Peoples R China

年份：2012

卷号：8

期号：3

起止页码：565

外文期刊名：JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000307834500004)】；

基金：This research has been supported by the National Natural Science Foundation under Grant(No. 61001200), the Foundation of Innovation Team of Science and Technology of Zhejiang Province of China (No. 2009R50024) and Natural Science Foundation and Education Department of Zhejiang under Grants( Nos.Y6100010).

语种：英文

外文关键词：Clustering; k-means algorithm; combinatorial optimization; data mining

摘要：As it is known that the performance of the k-means algorithm for data clustering largely depends on the choice of the Max-Min 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 a Max-Min scale for each cluster of patterns, and calculate Max-Min clustering centers according to the norm of the points. Experiments results show that the proposed algorithm provides good performance of clustering.