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Automatic topic identification using webpage clustering

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4 Author(s)
Xiaofeng He ; Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA ; C. H. Q. Ding ; Hongyuan Zha ; H. D. Simon

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

Published in:

Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on

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