A New Algorithm for Clustering Based on Particle Swarm Optimization and K-means

被引：9
|
作者：
Dong, Jinxin [1 ]
Qi, Minyong [1 ]
机构：
[1] Liaocheng Univ, Coll Comp Sci, Liaocheng, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL IV, PROCEEDINGS | 2009年
关键词：
clustering; K-means; particle swarm optimization;
D O I：
10.1109/AICI.2009.394
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Clustering is a technique that can divide data objects into meaningful groups. Particle swarm optimization is an evolutionary computation technique developed through a simulation of simplified social models. K-means is one of the popular unsupervised learning clustering algorithms. After analyzing particle swarm optimization and K-means algorithm, a new hybrid algorithm based on both algorithms is proposed. In the new algorithm, the next solution of the problem is generated by the better one of PSO and K-means but not PSO itself. It can make full use of the advantages of both algorithms, and can avoid shortcomings of both algorithms. The experimental results show the effectiveness of the new algorithm.
引用
收藏
页码：264 / 268
页数：5
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