Full Perfect Extension Pruning for Frequent Subgraph Mining

  • Christian Borgelt
  • Thorsten Meinl
Part of the Studies in Computational Intelligence book series (SCI, volume 165)

Summary

Mining graph databases for frequent subgraphs has recently developed into an area of intensive research. Its main goals are to reduce the execution time of the existing basic algorithms and to enhance their capability to find meaningful graph fragments. Here we present a method to achieve the former, namely an improvement of what we called “perfect extension pruning” in an earlier paper [4]. With this method the number of generated fragments and visited search tree nodes can be reduced, often considerably, thus accelerating the search.We describe the method in detail and present experimental results that demonstrate its usefulness.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christian Borgelt
    • 1
  • Thorsten Meinl
    • 2
  1. 1.European Center for Soft ComputingMieresSpain
  2. 2.Nycomed Chair for Bioinformatics and Information Mining Dept. of Computer and Information ScienceUniversity of KonstanzKonstanzGermany

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