JACIII Vol.15 No.2 pp. 125-133
doi: 10.20965/jaciii.2011.p0125


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

September 13, 2010
November 25, 2010
March 20, 2011
document clustering, document representation, rough sets, text mining

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) [1] 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.

Cite this article as:
Nguyen Chi Thanh, Koichi Yamada, and Muneyuki Unehara, “A Similarity Rough Set Model for Document Representation and Document Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.2, pp. 125-133, 2011.
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