计算机应用研究2012，Vol.29Issue(5)：1726-1728,3.DOI:10.3969/j.issn.1001-3695.2012.05.033
基于密度的K-means聚类中心选取的优化算法
Optimization algorithm of K-means clustering center of selection based on density
摘要
Abstract
Aiming at the problem of traditional K-means algorithm which is sensitive to initial clustering center and the number of cluster, this paper proposed a kind of optimization algorithm of initial clustering center selection. The algorithm was according to the distribution density of data and calculated the two vertical halfway points recently to determine the initial clustering center, then combined the equalization function to optimize the cluster number and got the optimal cluster. Used the standard UCI data sets as the contrast experiment objects, and found that the improved algorithm has the high accuracy and relative stability compared with traditional algorithm.关键词
K-均值/数据挖掘/聚类中心/垂直中点/密度Key words
K-means/data mining/clustering center/vertical halfway point/density分类
信息技术与安全科学引用本文复制引用
周炜奔,石跃祥..基于密度的K-means聚类中心选取的优化算法[J].计算机应用研究,2012,29(5):1726-1728,3.基金项目
湖南省教育厅创新平台开放基金资助项目(11K069) （11K069）
湖南省自然科学基金资助项目(07JJ6115) （07JJ6115）
智能制造湖南省高校重点实验室资助项目(20091M06) （20091M06）
