Full Perfect Extension Pruning for Frequent Subgraph Mining

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


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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  1. 1.
    Borgelt, C.: On Canonical Forms for Frequent Graph Mining. In: Proc. 3rd Int. Workshop on Mining Graphs, Trees and Sequences, MGTS 2005, Porto, Portugal, 1–12. ECML/PKDD 2005 Organization Committee, Porto, Portugal (2005)Google Scholar
  2. 2.
    Borgelt, C.: Combining Ring Extensions and Canonical Form Pruning. In: Proceedings of the 4th International Workshop on Mining and Learning in Graphs (MLG 2006), ECML/PKDD 2006 Organization Committee, Berlin, pp. 109–116 (2006)Google Scholar
  3. 3.
    Borgelt, C., Berthold, M.R.: Mining Molecular Fragments: Finding Relevant Substructures of Molecules. In: Proc. IEEE Int. Conf. on Data Mining, ICDM 2002, Maebashi, Japan, pp. 51–58. IEEE Press, Piscataway (2002)CrossRefGoogle Scholar
  4. 4.
    Borgelt, C., Meinl, T., Berthold, M.R.: Advanced Pruning Strategies to Speed Up Mining Closed Molecular Fragments. In: Proc. IEEE Conf. on Systems, Man and Cybernetics, SMC 2004, The Hague, Netherlands, IEEE Press, Piscataway (2004)Google Scholar
  5. 5.
    Cook, D.J., Holder, L.B.: Graph-Based Data Mining. IEEE Trans.on Intelligent Systems 15(2), 32–41 (2000)CrossRefGoogle Scholar
  6. 6.
    Di Fatta, G., Berthold, M.R.: Distributed Mining of Molecular Fragments. In: Workshop on Data Mining and the Grid, IEEE Int. Conf. on Data Mining, pp. 1–9. IEEE Press, Piscataway (2004)Google Scholar
  7. 7.
    Finn, P.W., Muggleton, S., Page, D., Srinivasan, A.: Pharmacore Discovery Using the Inductive Logic Programming System PROGOL. Machine Learning 30(2-3), 241–270 (1998)CrossRefGoogle Scholar
  8. 8.
    Hofer, H., Borgelt, C., Berthold, M.R.: Large Scale Mining of Molecular Fragments with Wildcards. Intelligent Data Analysis 8, 495–504 (2004)Google Scholar
  9. 9.
    Huan, J., Wang, W., Prins, J.: Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism. In: Proc. 3rd IEEE Int. Conf. on Data Mining, ICDM 2003, Melbourne, FL, pp. 549–552. IEEE Press, Piscataway (2003)Google Scholar
  10. 10.
    Index Chemicus — Subset from 1993. Institute of Scientific Information, Inc (ISI). Thomson Scientific, Philadelphia, PA, USA (1993),
  11. 11.
    Kramer, S., de Raedt, L., Helma, C.: Molecular Feature Mining in HIV Data. In: Proc. 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD 2001, San Francisco, CA, pp. 136–143. ACM Press, New York (2001)CrossRefGoogle Scholar
  12. 12.
    Kuramochi, M., Karypis, G.: Frequent Subgraph Discovery. In: Proc. 1st IEEE Int. Conf. on Data Mining, ICDM 2001, San Jose, CA, pp. 313–320. IEEE Press, Piscataway (2001)CrossRefGoogle Scholar
  13. 13.
    DTP AIDS Antiviral Screen (HIV Data Set) — Subset from 2001. Developmental Therapeutics Program (DTP), National Cancer Institute, USA (2001),
  14. 14.
    Nijssen, S., Kok, J.N.: A Quickstart in Frequent Structure Mining Can Make a Difference. In: Proc. 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD 2004, Seattle, WA, pp. 647–652. ACM Press, New York (2004)CrossRefGoogle Scholar
  15. 15.
    Washio, T., Motoda, H.: State of the Art of Graph-Based Data Mining. SIGKDD Explorations Newsletter 5(1), 59–68 (2003)CrossRefGoogle Scholar
  16. 16.
    Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: Proc. 2nd IEEE Int. Conf. on Data Mining, ICDM 2003, Maebashi, Japan, pp. 721–724. IEEE Press, Piscataway (2002)Google Scholar
  17. 17.
    Yan, X., Han, J.: Closegraph: Mining Closed Frequent Graph Patterns. In: Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD 2003, Washington, DC, pp. 286–295. ACM Press, New York (2003)CrossRefGoogle Scholar

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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