Global optimization in clustering using hyperbolic cross points

被引：4
|
作者：
Hu, Y.-K. [1 ]
Hu, Y. P.
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
来源：
PATTERN RECOGNITION | 2007年 / 40卷 / 06期
关键词：
clustering; fuzzy c-means; hard c-means; global optimization; hyperbolic cross points; genetic algorithms;
D O I：
10.1016/j.patcog.2006.11.005
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：1722 / 1733
页数：12
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