微型机与应用Issue(19)：17-19,23,4.
基于密度的优化初始聚类中心K-means算法研究
Study on K-means algorithm of optimized initial clustering centers based on density
何佳知 1谢颖华1
作者信息
- 1. 东华大学 信息科学与技术学院，上海 201620
- 折叠
摘要
Abstract
Aiming at the problem of the traditional K-means algorithm which generate its initial centers randomly from the data set, a method is proposed to optimize the initial center points through computing the density of objects. The algorithm computes the density of the area where the object belongs to, and then select K objects as the initial centers which has the highest density and has threshold distance to each other in high-density region. Also, the noise points in low-density region are treated separately. The experimental results demonstrate that the improved algorithm can get better clustering, and eliminate the sensitivity to the initial start centers.关键词
聚类/K-means 算法/密度/聚类中心/噪声点Key words
clustering/K-means algorithm/density/clustering center/noise points分类
信息技术与安全科学引用本文复制引用
何佳知,谢颖华..基于密度的优化初始聚类中心K-means算法研究[J].微型机与应用,2015,(19):17-19,23,4.
