Clustering with Local and Global Regularization

Fei Wang, Changshui Zhang, Tao Li

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

Subjects: 12. Machine Learning and Discovery; 9.3 Mathematical Foundations

Submitted: Apr 22, 2007


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