Clustering with Local and Global Regularization

Fei Wang, Changshui Zhang, Tao Li

Abstract: 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.