Automatic topic identification using webpage clustering

Grouping webpages into distinct topics is one way to organize the large amount of retrieved information on the web. In this paper, we report that based on 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 webpages. The clustering method is a state-of-art spectral graph partitioning method based on normalized cut criterion first developed for image segmentation.