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An Approach to Microscopic Clustering of Terms and Documents

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PRICAI 2002: Trends in Artificial Intelligence (PRICAI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2417))

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Abstract

In this paper, we present an approach to clustering in text-based information retrieval systems. The proposed method generates overlapping clusters, each of which is composed of subsets of associated terms and documents with normalized significance weights. In the paper, we first briefly introduce the probabilistic formulation of our clustering scheme and then show the procedure for cluster generation. We also report some experimental results, where the generated clusters are investigated in the framework of automatic text categorization.

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Author information

Authors and Affiliations

  1. National Institute of Informatics, Japan

    Akiko Aizawa

Authors
  1. Akiko Aizawa
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Editor information

Editors and Affiliations

  1. School of Information Science and Technology Department of Information and Communication Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Mitsuru Ishizuka

  2. School of Information Technology Knowledge Representation and Reasoning Unit (KRRU) Faculty of Engineering and Information Technology, Griffith University, PMB 50 Gold Coast Mail Centre, Queensland, 9726, Australia

    Abdul Sattar

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© 2002 Springer-Verlag Berlin Heidelberg

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Aizawa, A. (2002). An Approach to Microscopic Clustering of Terms and Documents. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_44

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  • DOI: https://doi.org/10.1007/3-540-45683-X_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44038-3

  • Online ISBN: 978-3-540-45683-4

  • eBook Packages: Springer Book Archive

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