The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Mining the Web's Link Structure
Computer
Enhancing the power of Web search engines by means of fuzzy query
Decision Support Systems - Web retrieval and mining
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
Web page ranking using link attributes
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
An Agent Based Method for Web Page Prediction
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Mining the web with hierarchical crawlers – a resource sharing based crawling approach
International Journal of Intelligent Information and Database Systems
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The existing search engines sometimes give unsatisfactory search result for lack of any categorization. If there is some means to know the preference of user about the search result and rank pages accordingly, the result will be more useful and accurate to the user. In the present paper a web page ranking algorithm is proposed based onsyntactic classification of web pages. The proposed approach mainly consists of three steps: select some properties of web pages based on user's demand, measure them, and give different weightage to each property during ranking for different types of pages. The existence of syntactic classification is supported by running fuzzy c-means algorithm and neural network classifier on a set of web pages. It has been demonstrated that, for different types of pages, the same query string has produced different page ranking.