Department of Computer Science
Mining Web site's clusters from link topology and site hierarchy
Foraging information in large and complex Web sites simply using keyword search usually results in unpleasant experience due to the overloaded search results. To support more effective information search, some descriptive abstractions of the Web sites (e.g., sitemaps) are mostly needed. However, their creation and maintenance normally requires recurrent manual effort due to the fast-changing Web contents. We extend the HITS algorithm and integrate hyperlink topology and Web site hierarchy to identify a hierarchy of Web page clusters as the abstraction of a Web site. As the algorithm is based on HITS, each identified cluster follows the bipartite graph structure, with an authority and hub pair as the cluster summary. The effectiveness of the algorithm has been evaluated using three different Web sites (containing /spl sim/6000-14000 Web pages) with promising results. Detailed interpretation of the experimental results as well as qualitative comparison with other related works are also included.
Topology, Clustering algorithms, Iterative algorithms, Web pages, Bipartite graph, Algorithm design and analysis, Search engines, Sun, Computer science, Keyword search
Source Publication Title
Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI’03)
This research is supported by UST AoE-IT Grant UST/AOE/01-02/1.
Link to Publisher's Edition
Cheung, K., & Sun, Y. (2003). Mining Web site's clusters from link topology and site hierarchy. Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI’03), 271-277. https://doi.org/10.1109/WI.2003.1241204