Accessibility of information on the Web
intelligence
Optimizing search by showing results in context
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modern Information Retrieval
Automatic Topic Identification Using Webpage Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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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In this paper, we present an Intelligent Cluster Search Engine System, called ICSE. This system is motivated by the observation that traditional search engines present to the users a set of non-classified web pages based on its ranking mechanism, and the unfortunate results are that they usually can not satisfy the need of users. For this reason, ICSE provides to the user a set taxonomic web pages in response to a user's query, and thus it would greatly help the users filter out irrelevant or redundant information. The proposed system can be divided into two parts. The first is the knowledge base constructed by Open Directory Project and Yahoo! Directory. The second is the fast clustering algorithm described herein for clustering the web pages. In addition, in response to a user's query, the proposed system will first send the query to a meta-search engine. Then, it will create a clustered document set using the given knowledge base and the clustering algorithm of ICSE. Our simulation result showed that the proposed system can enhance the relevance and coverage of the search results that the users need compared with traditional search engines.