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A Tree Distance Function Based on Multi-sets

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New Frontiers in Applied Data Mining (PAKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5433))

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Abstract

We introduce a tree distance function based on multi-sets. We show that this function is a metric on tree spaces, and we design an algorithm to compute the distance between trees of size at most n in O(n 2) time and O(n) space. Contrary to other tree distance functions that require expensive memory allocations to maintain dynamic programming tables of forests, our function can be implemented over simple and static structures. Additionally, we present a case study in which we compare our function with other two distance functions.

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Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Arnoldo José Müller-Molina, Kouichi Hirata & Takeshi Shinohara

Authors
  1. Arnoldo José Müller-Molina
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  2. Kouichi Hirata
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  3. Takeshi Shinohara
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Editor information

Editors and Affiliations

  1. School of Information Technologies, University of Sydney, NSW, Australia

    Sanjay Chawla

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1, Mihogaoka, Ibaraki, 567, Osaka, Japan

    Takashi Washio

  3. Division of Computing Science, Hokkaido University, 060-0814, Sapporo, Japan

    Shin-ichi Minato

  4. School of Medicine, Department of Medical Informatics, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shusaku Tsumoto

  5. Central Research Institute of Electric Power Industry, 2-11-1 Iwado-kita, Komae-shi, 201-8511, Tokyo, Japan

    Takashi Onoda

  6. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Seiji Yamada

  7. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, 567, Osaka, Japan

    Akihiro Inokuchi

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Müller-Molina, A.J., Hirata, K., Shinohara, T. (2009). A Tree Distance Function Based on Multi-sets. In: Chawla, S., et al. New Frontiers in Applied Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00399-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-00399-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00398-1

  • Online ISBN: 978-3-642-00399-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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