Study on Web-Page Classification Algorithm Based on Rough Set Theory

The large number of Web-page documents is comprise high dimensional huge text database with the development of Internet technology. But it is only a very small portion with the relevant users. The Web-page should be assigned to a category structure through the Web-page classification technology. it is not only convenient for customers to browse Web-page, but also easier to make Web-page seek through restriction search scope. Mining in high dimensional data is extraordinarily difficult because of the curse of dimensionality. We must adopt feature select to solve these problems. A algorithm is given in this paper to reduce the Web-page feature term and extract classification rule at last used attribute reduction on rough set theory. Experimental results show that this method has been greatly reduced feature vector space dimension and gotten easy-to-understand classification rules, and its accuracy is higher and the speed of classification is faster than based on the classification of vector comparison.