Kernel based K-medoids for clustering data with uncertainty

Authors:
Baoguo Yang;Yang Zhang
Affiliations:
College of Information Engineering, Northwest A&F University, Shaanxi, China;College of Information Engineering, Northwest A&F University, Shaanxi, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

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.