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To See the Wood for the Trees: Mining Frequent Tree Patterns

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Book cover Constraint-Based Mining and Inductive Databases

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3848))

Abstract

Various definitions and frameworks for discovering frequent trees in forests have been developed recently. At the heart of these frameworks lies the notion of matching, which determines if a pattern tree matches a tree in a data set. We compare four notions of tree matching for use in frequent tree mining and show how they are related to each other. Furthermore, we show how Zaki’s TreeMinerV algorithm can be adapted to employ three of the four notions of tree matching. Experiments on synthetic and real world data highlight the differences between the matchings.

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Author information

Authors and Affiliations

  1. Lab for Machine Learning, Institute of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 079, 79100, Freiburg, Germany

    Björn Bringmann

Authors
  1. Björn Bringmann
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Editor information

Editors and Affiliations

  1. INSA-Lyon, LIRIS CNRS UMR5205, F-69621, Villeurbanne, France

    Jean-François Boulicaut

  2. Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium

    Luc De Raedt

  3. HIIT, Helsinki University of Technology and, University of Helsinki, Finland

    Heikki Mannila

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© 2006 Springer-Verlag Berlin Heidelberg

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Bringmann, B. (2006). To See the Wood for the Trees: Mining Frequent Tree Patterns. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_3

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  • DOI: https://doi.org/10.1007/11615576_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31331-1

  • Online ISBN: 978-3-540-31351-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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