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Fuzzy Multiset Model and Methods of Nonlinear Document Clustering for Information Retrieval

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3131))

Abstract

As a model of information retrieval on the WWW, a fuzzy multiset model is overviewed and a family of fuzzy document clustering algorithms is developed. The fuzzy multiset model is enhanced in order to adapt clustering applications. The standard proximity measure of the cosine coefficient is generalized in the multiset model, and two basic objective functions of fuzzy c-means are considered. Moreover two methods of handling nonlinear classification is proposed: introduction of a cluster volume variable and a kernel trick used in support vector machines. A crisp c-means algorithm and clustering by competitive learning are also studied. A numerical example based on real documents is shown.

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Author information

Authors and Affiliations

  1. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, Ibaraki, 305-8573, Japan

    Sadaaki Miyamoto

  2. Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki, 305-8573, Japan

    Kiyotaka Mizutani

Authors
  1. Sadaaki Miyamoto
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  2. Kiyotaka Mizutani
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Editor information

Editors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute CSIC, Spanish National Research Council, Campus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10 Naka, Kunitachi, 186-0004, Tokyo, Japan

    Yasuo Narukawa

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© 2004 Springer-Verlag Berlin Heidelberg

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Miyamoto, S., Mizutani, K. (2004). Fuzzy Multiset Model and Methods of Nonlinear Document Clustering for Information Retrieval. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2004. Lecture Notes in Computer Science(), vol 3131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27774-3_26

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  • DOI: https://doi.org/10.1007/978-3-540-27774-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22555-3

  • Online ISBN: 978-3-540-27774-3

  • eBook Packages: Springer Book Archive

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