NSS-AKmeans: An Agglomerative Fuzzy K-Means Clustering Method with Automatic Selection of Cluster Number

摘要：

In this paper, we present a new Neighbor Sharing Selection based Agglomerative fuzzy K-means (NSSAKmeans) algorithm for learning optimal number of clusters and generating better clustering results. The NSS-AKmeans can identify high density areas and determine initial cluster centers from these areas with a neighbor sharing selection method. To select initial cluster centers, we propose an agglomeration energy (AE) factor for representing global density relationship of objects, and a Neighbors Sharing Factor (NSF) for estimating local neighbor sharing relationship of objects. Then we use the Agglomerative Fuzzy k-means clustering algorithm to further merge these initial centers to obtain the preferred number of clusters and generate better clustering results. Experimental results on various data sets have shown that the NSS-AKmeans was very effective in automatically identifying the true cluster number as well as producing accurate clustering results.

关键词： Neighbor Sharing Selection agglomeration energy Neighbors Sharing Factor initial cluster centers number of clusters

作者: Yanfeng Zhang Xiaofei Xu Yunming Ye

作者单位: Shenzhen Graduate School Harbin Institute of Technology, HIT Campus at Xili University Town, Shenzhe Department of Computer Science Harbin Institute of Technology, Harbin, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 32-38

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）