A Density-Based Method for Selection of the Initial Clustering Centers of K-means Algorithm

摘要：

　　The initial clustering centers of traditional K-means algorithm are randomly generated from a data set,clustering effect is not very stable.Aimed at this problem,this paper puts forward a kind of optimal selection of the initial clustering center of K-means algorithm based on density,by calculating the local density of each data point and the minimum distance between that point and any other point with higher local density,choose K points with higher local density as the initial clustering centers.Through the UCI standard database for contrast experiment,proved that the improved K-means algorithm can eliminate the dependence on the initial clustering center,has relatively higher accuracy and stability than the traditional algorithm.

关键词： Initial Clustering Centers K-means Algorithm Local Density

作者: Xin Du Ning Xu Cailan Zhou Shihui Xiao

作者单位: School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2509-2512

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）