Issue No.12 - December (2009 vol.21)

pp: 1665-1678

Fei Wang , Tsinghua University, Beijing

Changshui Zhang , Tsinghua University, Beijing

Tao Li , Florida International University, Miami

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.40

ABSTRACT

Clustering is an old research topic in data mining and machine learning. Most of the traditional clustering methods can be categorized as local or global ones. In this paper, a novel clustering method that can explore both the local and global information in the data set is proposed. The method, Clustering with Local and Global Regularization (CLGR), aims to minimize a cost function that properly trades off the local and global costs. We show that such an optimization problem can be solved by the eigenvalue decomposition of a sparse symmetric matrix, which can be done efficiently using iterative methods. Finally, the experimental results on several data sets are presented to show the effectiveness of our method.

INDEX TERMS

Clustering, local learning, smoothness, regularization.

CITATION

Fei Wang, Changshui Zhang, Tao Li, "Clustering with Local and Global Regularization",

*IEEE Transactions on Knowledge & Data Engineering*, vol.21, no. 12, pp. 1665-1678, December 2009, doi:10.1109/TKDE.2009.40REFERENCES