文献类型：期刊文献

英文题名：Global optimization in clustering using hyperbolic cross points

作者：Hu, Y.-K.[1]; Hu, Y.P.[2]

第一作者：Hu, Y.-K.

通讯作者：Hu, YK[1]

机构：[1]Georgia So Univ, Dept Math Sci, Statesboro, GA 30460 USA;[2]Beijing Union Univ, Expt & Training Base, Beijing 100101, Peoples R China

第一机构：Georgia So Univ, Dept Math Sci, Statesboro, GA 30460 USA

通讯机构：[1]corresponding author), Georgia So Univ, Dept Math Sci, Statesboro, GA 30460 USA.

年份：2007

卷号：40

期号：6

起止页码：1722-1733

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20071110490994);Scopus(收录号：2-s2.0-33947182396);WOS:【SCI-EXPANDED(收录号:WOS:000245745000008)】；

语种：英文

外文关键词：clustering; fuzzy c-means; hard c-means; global optimization; hyperbolic cross points; genetic algorithms

摘要：Erich Novak and Klaus Ritter developed in 1996 a global optimization algorithm that uses hyperbolic cross points (HCPs). In this paper we develop a hybrid algorithm for clustering called CMHCP that uses a modified version of this HCP algorithm for global search and the alternating optimization for local search. The program has been tested extensively with very promising results and high efficiency. This provides a nice addition to the arsenal of global optimization in clustering. In the process, we also analyze the smoothness of some reformulated objective functions. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.