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Discovering Frequent Substructures in Large Unordered Trees

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Discovery Science (DS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2843))

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Abstract

In this paper, we study a frequent substructure discovery problem in semi-structured data. We present an efficient algorithm Unotthat computes all frequent labeled unordered trees appearing in a large collection of data trees with frequency above a user-specified threshold. The keys of the algorithm are efficient enumeration of all unordered trees in canonical form and incremental computation of their occurrences. We then show that Unotdiscovers each frequent pattern T in O(kb 2 m) per pattern, where k is the size of T, b is the branching factor of the data trees, and m is the total number of occurrences of T in the data trees.

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Author information

Authors and Affiliations

  1. Kyushu University, Fukuoka, 812–8581, JAPAN

    Tatsuya Asai & Hiroki Arimura

  2. National Institute of Informatics, Tokyo, 101–8430, JAPAN

    Takeaki Uno

  3. Gunma University, Kiryu-shi, Gunma, 376–8515, JAPAN

    Shin-ichi Nakano

Authors
  1. Tatsuya Asai
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  2. Hiroki Arimura
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  3. Takeaki Uno
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  4. Shin-ichi Nakano
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Editor information

Editors and Affiliations

  1. FG Knowledge Engineering, FB Informatik, Technical University Darmstadt, Hochschulstr. 10, 64289, Darmstadt

    Gunter Grieser

  2. Meme Media Laboratory, Hokkaido University, N13 W8, 0608628, Sapporo, Japan

    Yuzuru Tanaka

  3. Graduate School of Informatics, Kyoto University Yoshida Honmachi, Sakyo-ku, 606-850, Kyoto, Japan

    Akihiro Yamamoto

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© 2003 Springer-Verlag Berlin Heidelberg

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Asai, T., Arimura, H., Uno, T., Nakano, Si. (2003). Discovering Frequent Substructures in Large Unordered Trees. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_6

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  • DOI: https://doi.org/10.1007/978-3-540-39644-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20293-6

  • Online ISBN: 978-3-540-39644-4

  • eBook Packages: Springer Book Archive

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