An initial point selection Algorithm for K-Means Clustering

摘要：

　　Aiming at the problem of K-Means algorithm which is sensitive to select initial clustering center,this paper proposes a kind of initial point of K-Means algorithm.The algorithm processes the properties of the data objects,which determines the density of data object by counting the number of similar data objects and selects the center of categories according to the density of data object.The cluster numbers given and the UCI standard sets of data and the random data sets used,the clustering results demonstrate that the proposed algorithm has good stability,accuracy.

关键词： K-Means algorithm Clustering center Data mining Density

作者: Leqiang Bai Yanyao Zhou Shihong Zhang

作者单位: Information &control Engineering Faculty Shenyang Jianzhu University Shenyang, China

会议类型: 国际会议

会议名称: 2013 3rd International Symposium on Chemical Engineering and Material Properties(2013第三届化学工程和材料性能国际研讨会)(ISCEMP 2013)

会议地点: 三亚

会议语种:英文

页码: 735-738

在线出版日期: 2013-06-22（万方平台首次上网日期，不代表论文的发表时间）