2000 | OriginalPaper | Buchkapitel
Hierarchical Document Clustering Based on Tolerance Rough Set Model
verfasst von : Saori Kawasaki, Ngoc Binh, Tu Bao
Erschienen in: Principles of Data Mining and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.