A method of dynamically determining the number of clusters and cluster centers

被引：0

作者：

Shao Xiongkai ^{[1
]}

Pi Ling ^{[1
]}

Liu Lianzhou ^{[1
]}

机构：

[1] Hubei Univ Technol, Sch Comp Sci, Hubei Wuhan, Peoples R China

来源：

PROCEEDINGS OF THE 2013 8TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2013) | 2013年

关键词：

k-means algorithm; dynamically determining the number of clusters; text similarity;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Text clustering is an important technology in the field of data mining. The traditional K-means algorithm is sensitive to the number of clusters, and there is a limitation that the result of randomly initializing cluster centers is not stable. This paper presents a method of dynamically determining the number of clusters and cluster centers based on text similarity matrix. The experiment results show that the method works well and improves the K-means algorithm's accuracy and adaptability.

引用

页码：283 / 286

页数：4