Improved K-means Algorithm to Quickly Locate Optimum Initial Clustering Number K

摘要：

K-means algorithm is often used as a clustering algorithm,but it is vulnerable to the impact of the clustering number k.To eliminate the effect,a method seeking optimum initial clustering number k rapidly is put forward for the k-means algorithm.This method is accomplished by subtractive clustering to determine the optimal initial clustering k.The experiments to the data inside the public database UCI and TE data show that the improved k-means algorithm can eliminate the sensitivity to the initial cluster number k.The clustering speed and precision are improved.

作者: YANG Qing LIU Ye ZHANG Dongxu LIU Chang

作者单位: School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,P.R.China C School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,P.R.China

会议类型: 国际会议

会议名称: The 30th Chinese Control Conference(第三十届中国控制会议)

会议地点: 烟台

会议语种:英文

页码: 1-4

在线出版日期: 2011-07-01（万方平台首次上网日期，不代表论文的发表时间）