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Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

Many documents such as Web documents or XML files have no rigid structure. Such semistructured documents have been rapidly increasing. We propose a new method for discovering frequent tree structured patterns in semistructured Web documents. We consider the data mining problem of finding all maximally frequent tag tree patterns in semistructured data such as Web documents. A tag tree pattern is an edge labeled tree which has hyperedges as variables. An edge label is a tag or a keyword in Web documents, and a variable can be substituted by any tree. So a tag tree pattern is suited for representing tree structured patterns in semistructured Web documents. We present an algorithm for finding all maximally frequent tag tree patterns. Also we report some experimental results on XML documents by using our algorithm.

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Author information

Authors and Affiliations

  1. Faculty of Information Sciences, Hiroshima City University, Hiroshima, 731-3194, Japan

    Tetsuhiro Miyahara, Tomoyuki Uchida, Kenichi Takahashi & Hiroaki Ueda

  2. Department of Informatics, Kyushu University, Kasuga, 816-8580, Japan

    Takayoshi Shoudai

Authors
  1. Tetsuhiro Miyahara
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  2. Takayoshi Shoudai
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  3. Tomoyuki Uchida
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  4. Kenichi Takahashi
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  5. Hiroaki Ueda
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Miyahara, T., Shoudai, T., Uchida, T., Takahashi, K., Ueda, H. (2001). Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_8

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  • DOI: https://doi.org/10.1007/3-540-45357-1_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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