Clustering Data with Particle Swarm Optimization Using a New Fitness

被引：0

作者：

Toreini, Ehsan ^{[1
]}

Mehrnejad, Maryam ^{[2
]}

机构：

[1] Islamic Azad Univ, Mashhad Branch, Dept Comp Engn, Mashhad, Iran

[2] Univ Mashhad, Fac Engn Ferdowsi, Dept Comp, Mashhad, Iran

来源：

2011 3RD CONFERENCE ON DATA MINING AND OPTIMIZATION (DMO) | 2011年

关键词：

Data Clustering; Particle Swarm Optimization; Evolutionary Clustering; Clustering; FUZZY C-MEANS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data clustering has been studied for a long time and every day trends are proposed for better outcomes in this field. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. In this paper, we consider a new fitness function for our PSO-based clustering method and compared it with the previous ones. Experimental results show that our method has better outcomes than the other ones.

引用

页码：266 / 270

页数：5

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