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Clustering with Local and Global Regularization
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DOI：10.1109/TKDE.2009.40.png)
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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.
Keyword:
Clustering
local learning
smoothness
regularization
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