An efficient clustering framework for relevant web information
Pages 450 - 456
Abstract
As the amount of available information on the Internet grows, it is becoming increasingly difficult for users to find information that is relevant to their needs. Against this backdrop, a need for an automated tool that can find information quickly and easily has surfaced. In this paper, we propose a Clustering Framework for crawling and clustering the necessary information from Web pages. The proposed clustering framework consists of three modules: a preprocessing module, clustering module and community module. Using this framework, we are able to automatically cluster Web pages according to topic and rank them in terms of relevance. We describe this framework, and show the results of our preliminary validation work.
References
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Zamir, O. and Etzioni, O., "Web Document Clustering: a Feasibility Demonstration," In Proc. 19 Int'l ACM SIGR Conf. on Research and Development in Information Retrieval, pp.46--54, Melbourne, Australia, June 1998.
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Van Rijsbergen, C. J. "Information Retrieval", London: Butterworths, 1979.
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William B. Frakes, Ricardo Baeza-Yates, "Information Retrieval: Data Structures & Algorithms", Prentice Hall, 1992
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Peter Willett, "Recent Trends in Hierarchical Document Clustering: A Critical Review", Information Processing & Management Vol. 24, No. 5, 1988
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Sholom M. Weiss, N. Indurkhya, T. Zhang and Fred J. Damerau, "Text mining: Predictive Methods for Analyzing Unstructured Information", Springer, 2005.
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Ok-Ran Jeong, Seunghwa Lee, Eunseok Lee, "A Framework for Automatic Topic Discovery on SubWebs" FSKD'08( the 5th International Conference on Fuzzy Systems and Knowledge Discovery), Jinan, China, Vol. 2, pp. 332--337, Oct.18, 2008
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- An efficient clustering framework for relevant web information
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February 2009
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Published: 15 February 2009
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ICUIMC '09
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ICUIMC '09: The 3rd International Conference on Ubiquitous Information Management and Communication
January 15 - 16, 2009
Suwon, Korea
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