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Analysis of Clustering Algorithms for Web-Based Search

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Practical Aspects of Knowledge Management (PAKM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2569))

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Abstract

Automatic document categorization plays a key role in the development of future interfaces for Web-based search. Clustering algorithms are considered as a technology that is capable of mastering this “ad-hoc” categorization task. This paper presents results of a comprehensive analysis of clustering algorithms in connection with document categorization. The contributions relate to exemplarbased, hierarchical, and density-based clustering algorithms. In particular, we contrast ideal and real clustering settings and present runtime results that are based on efficient implementations of the investigated algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Paderborn University, D-33095, Paderborn, Germany

    Sven Meyer zu Eissen & Benno Stein

Authors
  1. Sven Meyer zu Eissen
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  2. Benno Stein
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Editor information

Editors and Affiliations

  1. Department of Knowledge Engineering, University of Vienna, Brünner Str. 72, 1210, Vienna, Austria

    Dimitris Karagiannis

  2. Business Operation Systems, Esslenstr. 3, 8280, Kreuzlingen, Switzerland

    Ulrich Reimer

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© 2002 Springer-Verlag Berlin Heidelberg

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Eissen, S.M.z., Stein, B. (2002). Analysis of Clustering Algorithms for Web-Based Search. In: Karagiannis, D., Reimer, U. (eds) Practical Aspects of Knowledge Management. PAKM 2002. Lecture Notes in Computer Science(), vol 2569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36277-0_16

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  • DOI: https://doi.org/10.1007/3-540-36277-0_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00314-4

  • Online ISBN: 978-3-540-36277-7

  • eBook Packages: Springer Book Archive

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