Clustering Documents with Large Overlap of Terms into Different Clusters based on Similarity Rough Set Model
نویسندگان
چکیده
Similarity rough set model for document clustering (SRSM) uses a generalized rough set model based on similarity relation and term co-occurrence to group documents in the collection into clusters. The model is extended from tolerance rough set model (TRSM) (Ho and Funakoshi, 1997). The SRSM methods have been evaluated and the results showed that it perform better than TRSM. However, in document collections where there are words overlapped in different document classes, the effect of SRSM is rather small. In this paper we propose a method to improve the performance of SRSM method in such document collections.
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