Initializing K-means Clustering Using Affinity Propagation

摘要：

K-means clustering is widely used due to its fast convergence, but it is sensitive to the initial condition. Therefore, many methods of initializing K-means clustering have been proposed in the literatures. Compared with Kmeans clustering, a novel clustering algorithm called af.nity propagation (AP clustering) has been developed by Frey and Dueck, which can produce a good set of cluster exemplars with fast speed. Taking the convergence property of K-means and the good performance of af.nity propagation, we presented a new clustering strategy which can produce much lower squared error than AP and standard K-means: initializing K-means clustering using cluster exemplars produced by AP. Numerical experiments indicated that such combined method outperforms not only AP and original K-means clustering, but also Kmeans clustering with sophisticated initial conditions designed by various methods.

关键词： k-means k-centers affinity propagation convergence

作者: Yan Zhu Jian Yu Caiyan Jia

作者单位: Department of Computer Science Beijing Jiaotong University,100044 Beijing, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）