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Extraction of Fuzzy Clusters from Weighted Graphs

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

A spectral graph method is presented for partitioning of nodes in a graph into fuzzy clusters on the basis of weighted adjacency matrices. Extraction of a fuzzy cluster from a node set is formulated by an eigenvalue problem and clusters are extracted sequentially from major one to minor ones. A clustering scheme is devised at first for undirected graphs and it is next extended to directed graphs and also to undirected bipartite ones. These clustering methods are applied to analysis of a link structure in Web networks and image retrieval queried by keywords or sample images. Extracted structure of clusters is visualized by a multivariate exploration method called the correspondence analysis.

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Author information

Authors and Affiliations

  1. Kyushu Institute of Design, Fukuoka, 815-8540, Japan

    Seiji Hotta, Kohei Inoue & Kiichi Urahama

Authors
  1. Seiji Hotta
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  2. Kohei Inoue
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  3. Kiichi Urahama
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Hotta, S., Inoue, K., Urahama, K. (2000). Extraction of Fuzzy Clusters from Weighted Graphs. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_51

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  • DOI: https://doi.org/10.1007/3-540-45571-X_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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