An Improved K-means Algorithm Based on Weighted Euclidean Distance

被引：0
|
作者：
Ge, Fuhua [1 ]
Luo, Yi [1 ]
机构：
[1] Yunnan Univ, Informat Coll, Kunming, Peoples R China
来源：
2012 THIRD INTERNATIONAL CONFERENCE ON THEORETICAL AND MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE (ICTMF 2012) | 2013年 / 38卷
关键词：
Clustering; K-means Algorithm; Clustering Stability;
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：
The exiting problems of K-means clustering algorithm are carefully researched, and an improved K-means algorithm based on weighted Euclidean distance is presented. The algorithm have overcome the K-means algorithm unsteady clustering problem. The experiments demonstrate the improved algorithm is more stable and more accurate.
引用
收藏
页码：117 / 120
页数：4
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