Regularized clustering for documents

Fei Wang, Changshui Zhang, Tao Li
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
In recent years, document clustering has been receiving more and more attentions as an important and fundamental technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. In this paper, we propose a novel method for clustering documents using regularization. Unlike traditional globally regularized clustering methods, our method first construct a local regularized linear label predictor for each document vector, and then combine
more » ... those local regularizers with a global smoothness regularizer. So we call our algorithm Clustering with Local and Global Regularization (CLGR). We will show that the cluster memberships of the documents can be achieved by eigenvalue decomposition of a sparse symmetric matrix, which can be efficiently solved by iterative methods. Finally our experimental evaluations on several datasets are presented to show the superiorities of CLGR over traditional document clustering methods.
doi:10.1145/1277741.1277760 dblp:conf/sigir/WangZL07 fatcat:6md6hp3b6bevllkzdys2vi4dvm