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基于混沌社会演化算法的文本聚类新方法
引用本文:郝占刚,王正欧. 基于混沌社会演化算法的文本聚类新方法[J]. 系统工程学报, 2007, 22(1): 109-112
作者姓名:郝占刚  王正欧
作者单位:1. 山东工商学院工商管理学院,山东,烟台,264005;天津大学系统工程研究所,天津,300072
2. 天津大学系统工程研究所,天津,300072
摘    要:用K均值算法进行文本聚类通常只能以局部最优结束,很难找到全局最优.文章提出了一种基于混沌社会演化算法的文本聚类新方法.在该方法中提出了认知主体在聚类中对范式继承的方式,在认知主体对范式的背叛中提出一种混沌变异算子.实验证明该方法不但能有效地提高文本聚类的效率而且能有效地提高文本聚类的精度.

关 键 词:文本聚类  混沌社会演化算法  K均值算法
文章编号:1000-5781(2007)01-0109-04
收稿时间:2006-03-06
修稿时间:2006-03-062006-10-30

New text clustering method based on chaotic social evolutionary programming algorithm
HAO Zhan-gang,WANG Zheng-ou. New text clustering method based on chaotic social evolutionary programming algorithm[J]. Journal of Systems Engineering, 2007, 22(1): 109-112
Authors:HAO Zhan-gang  WANG Zheng-ou
Affiliation:1. Business and Management School, Shandong Institute of Business and Technology, Yantai 264005, China; 2.Institute of Systems Engineering, Tianjin University, Tianjin 300072, China
Abstract:In the text clustering,K-means clustering algorithm often falls into a local optimum and it is very difficult to find the global optimum.This paper proposes a new text clustering method based on the CSEP(chaotic social evolutionary programming) algorithm.In this method,we present a manner of that a cognitive agent inherits a paradigm in clustering,and a chaotic mutation operator is used for the betrayal of a cognitive agent to a paradigm.The experiments demonstrate that the present method not only can effectively improve the efficiency of text clustering,but also can effectively improve precision of text clustering.
Keywords:text clustering  chaotic social evolutionary programming  K-means algorithm
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