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Term weighting in query-based document clustering

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Book cover Advances in Databases and Information Systems (ADBIS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1475))

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Abstract

Search agents in the World-Wide Web are able to find documents which match user-submitted queries. As the number of matching documents returned by the agent is often very large, it would be easier for the user to browse through clusters rather than individual documents when searching for the documents which really satisfy his information needs. In this paper we introduce a new approach to term weighting for the document similarity measures needed in clustering. The approach is based on term occurrence frequencies within the set of documents which match an initial query. We suggest that this approach might perhaps be useful in noise reduction in the very rich and heterogenous environment of the Web.

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References

  1. M. A. Hearst & J. O. Pedersen: “Reexamining the Cluster Hypothesis: Scatter/Gather on Retrieval Results”. In Proc. of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Zurich, Switzerland 1996.

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  2. K. Korpimies & E. Ukkonen: “Searching for General Documents”. In Proc. of the International Conference on Flexible Query Answering Systems (FQAS’98). Lecture Notes in Artificial Intelligence, Springer 1998.

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  3. Gerald Salton: “Automatic text processing: the transformation, analysis, and retrieval of information by computer”. Addison-Wesley, Reading, MA, 1989.

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  4. O. Zamir, O. Etzioni, O. Madani & R. M. Karp: “Fast and Intuitive Clustering of Web Documents”. In Proc. the Third International Conference on Knowledge Discovery and Data Mining. Newport Beach, CA, 1997.

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Authors and Affiliations

  1. Department of Computer Science, University of Helsinki, PO BOX 26, FIN-00014, Finland

    Kai Korpimies & Esko Ukkonen

Authors
  1. Kai Korpimies
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  2. Esko Ukkonen
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Witold Litwin Tadeusz Morzy Gottfried Vossen

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© 1998 Springer-Verlag Berlin Heidelberg

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Korpimies, K., Ukkonen, E. (1998). Term weighting in query-based document clustering. In: Litwin, W., Morzy, T., Vossen, G. (eds) Advances in Databases and Information Systems. ADBIS 1998. Lecture Notes in Computer Science, vol 1475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057726

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  • DOI: https://doi.org/10.1007/BFb0057726

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64924-3

  • Online ISBN: 978-3-540-68309-4

  • eBook Packages: Springer Book Archive

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