Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
A clustering algorithm for asymmetrically related data with applications to text mining
Proceedings of the tenth international conference on Information and knowledge management
Generating hierarchical summaries for web searches
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 13th international conference on World Wide Web
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
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Because of the improvement of the technology of search engines, and the massively increase of the number of web pages, the results returned by the search engines are always mixed and disordered. Especially for the queries with multiple topics, the mixed and disorderly situation of the search results would be more obvious. The search engines can return information of several hundred to thousand of the pages' titles, snippets and URLs. Almost all of the technologies about search result clustering must attain further information from the contents of the returned lists. However, long execution time is not permitted for a real-time clustering system. In this paper we propose some methods with better efficiency to improve the previous technologies. We utilize and augment information that search engines returned and use entropy calculation to attain the term distribution in snippets. We also propose several new methods to attain better clustered search results and reduce execution time. Our experiments indicate that these proposed methods obtain the better clustered results.