A Similarity Rough Set Model for Document Representation and Document Clustering
Nguyen Chi Thanh, Koichi Yamada, and Muneyuki Unehara
Department of Management and Information System Science, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata 940-2188, Japan
Document clustering is a textmining technique for unsupervised document organization. It helps the users browse and navigate large sets of documents. Ho et al. proposed a Tolerance Rough Set Model (TRSM)  for improving the vector space model that represents documents by vectors of terms and applied it to document clustering. In this paper we analyze their model to propose a new model for efficient clustering of documents. We introduce Similarity Rough Set Model (SRSM) as another model for presenting documents in document clustering. The model is evaluated by experiments on test collections. The experiment results show that the SRSM document clusteringmethod outperforms the one with TRSM and the results of SRSM are less affected by the value of parameter than TRSM.
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