Evaluating contents-link coupled web page clustering for web search results
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While web search engine could retrieve information on the Web for a specific topic, users have to step a long ordered list in order to locate the needed information, which is often tedious and less efficient. In this paper, we propose a new link-based clustering approach to cluster search results returned from Web search engine by exploring both co-citation and coupling. Unlike document clustering algorithms in IR that are based on common words/phrases shared among documents, our approach is based on common links shared by pages. We also extend standard clustering algorithm, K-means, to make it more natural to handle noises and apply it to web search results. By filtering some irrelevant pages, our approach clusters high quality pages in web search results into semantically meaningful groups to facilitate users'accessing and browsing. Preliminary experiments and evaluations are conducted to investigate its effectiveness. The experimental results show that link-based clustering of web search results is promising and beneficial.