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Extension of Graph-Based Induction for General Graph Structured Data

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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 machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from directed graph data by stepwise pair expansion (pairwise chunking). In this paper, we expand the capability of the Graph-Based Induction to handle not only tree structured data but also multi-inputs/outputs nodes and loop structure (including a self-loop) which cannot be treated in the conventional way. The method is verified to work as expected using artificially generated data and we evaluated experimentally the computation time of the implemented program. We, further, show the effectiveness of our approach by applying it to two kinds of the real-world data: World Wide Web browsing data and DNA sequence data.

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Author information

Authors and Affiliations

  1. I.S.I.R., Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Takashi Matsuda, Tadashi Horiuchi, Hiroshi Motoda & Takashi Washio

Authors
  1. Takashi Matsuda
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  2. Tadashi Horiuchi
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  3. Hiroshi Motoda
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  4. Takashi Washio
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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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Matsuda, T., Horiuchi, T., Motoda, H., Washio, T. (2000). Extension of Graph-Based Induction for General Graph Structured Data. 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_49

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  • DOI: https://doi.org/10.1007/3-540-45571-X_49

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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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