Automatic topic identification using webpage clustering
Xiaofeng He, Chris H. Q. Ding, Hongyuan Zha, Horst D. Simon · 2002
Grouping Web pages into distinct topics is one way of organizing the large amount of retrieved information on the Web. In this paper, we report that, based on a similarity metric, which incorporates textual information, hyperlink structure and co-citation relations, an unsupervised clustering method can automatically and effectively identify relevant topics, as shown in experiments on several retrieved sets of Web pages. The clustering method is a state-of-art spectral graph partitioning method based on the normalized cut criterion first developed for image segmentation.