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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
Cook, D.J., Holder, L.B.: Graph-Based Data Mining. IEEE Trans.on Intelligent Systems 15(2), 32–41 (2000)
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)
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)
Hofer, H., Borgelt, C., Berthold, M.R.: Large Scale Mining of Molecular Fragments with Wildcards. Intelligent Data Analysis 8, 495–504 (2004)
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)
Index Chemicus — Subset from 1993. Institute of Scientific Information, Inc (ISI). Thomson Scientific, Philadelphia, PA, USA (1993), http://www.thomsonscientific.com/products/indexchemicus/
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)
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)
DTP AIDS Antiviral Screen (HIV Data Set) — Subset from 2001. Developmental Therapeutics Program (DTP), National Cancer Institute, USA (2001), http://dtp.nci.nih.gov/docs/aids/aids_data.html
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)
Washio, T., Motoda, H.: State of the Art of Graph-Based Data Mining. SIGKDD Explorations Newsletter 5(1), 59–68 (2003)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Borgelt, C., Meinl, T. (2009). Full Perfect Extension Pruning for Frequent Subgraph Mining. In: Zighed, D.A., Tsumoto, S., Ras, Z.W., Hacid, H. (eds) Mining Complex Data. Studies in Computational Intelligence, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88067-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-88067-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88066-0
Online ISBN: 978-3-540-88067-7
eBook Packages: EngineeringEngineering (R0)