Research on Improved K-means Clustering Algorithm

被引：0
|
作者：
Zhang, Yinsheng [1 ]
Shan, Huilin [1 ]
Li, Jiaqiang [1 ]
Zhou, Jie [1 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Nanjing 210044, Jiangsu, Peoples R China
来源：
MEMS, NANO AND SMART SYSTEMS, PTS 1-6 | 2012年 / 403-408卷
关键词：
Pattern Recognition; Hierarchical Clustering; K-means Clustering Algorithm; !text type='Java']Java[!/text;
D O I：
10.4028/www.scientific.net/AMR.403-408.1977
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The traditional K-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster center. Hierarchical clustering algorithm can be used to generate the initial cluster center of K-means clustering algorithm. The geometric features of input data can achieve a good distribution by means of pretreatment and feature extraction and selection. In the learning of fuzzy neural network, Java language is used to write source code of the algorithm. The experimental results show that new algorithm has improved the clustering quality effectively.
引用
收藏
页码：1977 / 1980
页数：4
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