Kernel Based K-Medoids for Clustering Data with Uncertainty

摘要：

Uncertain data is ubiquitous in real-world applications due to various causes. In recent years, clustering uncertain data has been paid more attention by the research community, and the classical clustering algorithms based on partition, density and hierarchy have been extended to handle the uncertain data. However, these extended algorithms usually work in the input space. In this paper, to well explore the inherent data pattern in the high dimensional feature space, we propose a kernel based K-medoids algorithm for clustering uncertain data. Extensive experiments performed on synthetic and several real datasets demonstrate that our kernel based method has higher clustering accuracy than the state-of-the-art UK-medoids algorithm. Also, it signifies that the uncertain data pattern in the new feature space could be well presented when the kernel function and the K-medoids algorithm are effectively incorporated.

关键词： Uncertain data Clustering Kernel trick K-medoids

作者: Baoguo Yang Yang Zhang

作者单位: College of Information Engineering Northwest A&F University,Yangling 712100 Shaanxi China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 246-253

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）