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Clustering is a powerful tool for knowledge discovery in text collections. The quality of document clustering depends not only on clustering algorithms but also on document representation models. We develop a hierarchical document clustering algorithm based on a tolerance rough set model (TRSM) for representing documents, which offers a way of considering semantics relatedness between documents. The results of validation and evaluation of this method suggest that this clustering algorithm can be well adapted to text mining.

Hierarchical Document Clustering Based on Tolerance Rough Set Model