Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Methodologies for Intelligent Systems

ISMIS 2011: Foundations of Intelligent Systems pp 326–335Cite as

From Connected Frequent Graphs to Unconnected Frequent Graphs

  • Conference paper

  • 3707 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6804))

Abstract

We present the UFC (Unconnected From Connected) algorithm which discovers both connected and disconnected frequent graphs. It discovers connected frequent graphs by means of any existing graph mining algorithm and then joins these graphs with each other creating unconnected frequent graphs with increasing number of connected components. We compare our method with previously proposed UGM algorithm and a gSpan variation.

This work is supported by the National Centre for Research and Development (NCBiR) under Grant No. SP/I/1/77065/10 by the Strategic scientific research and experimental development program: Interdisciplinary System for Interactive Scientific and Scientific-Technical Information.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993, pp. 207–216 (1993)

    Google Scholar 

  2. Borgelt, C., Berthold, M.R.: Mining Molecular Fragments: Finding Relevant Substructures of Molecules. In: ICDM, vol. 00:51 (2002)

    Google Scholar 

  3. Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraph in the presence of isomophism (2003)

    Google Scholar 

  4. Huan, J., Wang, W., Prins, J., Yang, J.: SPIN: mining maximal frequent subgraphs from graph databases. In: KDD 2004, pp. 581–586 (2004)

    Google Scholar 

  5. Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data (2000)

    Google Scholar 

  6. Inokuchi, A., Washio, T., Nishimura, K., Motoda, H.: A fast algorithm for mining frequent connected subgraphs. Research Report RT0448 (2002)

    Google Scholar 

  7. Kuramochi, G., Karypis, M.: Frequent subgraph discovery. In: Proceedings IEEE International Conference on Data Mining, 2001, ICDM 2001, pp. 313–320 (2001)

    Google Scholar 

  8. Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: KDD 2004, pp. 647–652 (2004)

    Google Scholar 

  9. Skonieczny, L.: Mining of Unconnected Frequent Graphs with Direct Subgraph Isomorphism Tests. In: ICMMI 2009 (2009)

    Google Scholar 

  10. Ting, R., Bailey, J.: Mining minimal contrast subgraph patterns. In: Jonker, W., Petković, M. (eds.) SDM 2006. LNCS, vol. 4165, pp. 639–643. Springer, Heidelberg (2006)

    Google Scholar 

  11. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: ICMD, vol. 00:721 (2002)

    Google Scholar 

  12. Yan, X., Han, J.: CloseGraph: mining closed frequent graph patterns. In: KDD 2003, pp. 286–295. ACM , New York (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Łukasz Skonieczny

Authors
  1. Łukasz Skonieczny
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology,, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

  2. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybinski

  3. University of Warsaw, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19,, 00-665, Warsaw, Poland

    Zbigniew W. Raś

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Skonieczny, Ł. (2011). From Connected Frequent Graphs to Unconnected Frequent Graphs. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21916-0_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21915-3

  • Online ISBN: 978-3-642-21916-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation