A Particle Swarm Optimization K-Means Algorithm for Mongolian Elements Clustering

被引：0
|
作者：
Hua, Chun [1 ,2 ]
Wei, Wu [3 ]
机构：
[1] Dalian Univ Technol, Dalian 116024, Peoples R China
[2] Inner Mongolia Univ Nationalities, Coll Comp Sci & Technol, Tongliao 028043, Peoples R China
[3] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
来源：
2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年
关键词：
K-Means; particle swarm optimization; mongolian syllables; GENETIC ALGORITHM;
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：
Text clustering is an important research area of clustering technique. Clustering analysis groups the text according to similar characteristics, so the text in the same clusters have the greatest similarity, while the text in different clusters have the greatest dissimilarity. In this paper, we proposed a hybrid clustering technique called PSOKM that combined particle swarm optimization algorithm with K-Means. Numerical experiments show that our proposed algorithm outperforms than existing others.
引用
收藏
页码：1559 / 1564
页数：6
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