Web Document Classification Based on Rough Set Latent Semantic Indexing
Xianghua Fu · Jisuanji gongcheng · 2004
Rough set theory is a mathematical tool to deal with uncertain or vague knowledge. An approach to Web document classification based on rough set latent semantic indexing is proposed. Firstly, Web documents, which are denoted by vector space model reduced document feature set. Then, information filtering and latent semantic indexing are conducted by singular value decomposition of matrix. Generating classification rule by attribution reduces algorithm. Finally, the documents are classified with multiple knowledge bases. The experiment results and the comparison with others show tha this Web document classification has good classification performance.