Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Greedy Search Approach of Graph Mining

  • Lawrence Holder
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_354


 Greedy search is an efficient and effective strategy for searching an intractably large space when sufficiently informed heuristics are available to guide the search. The space of all subgraphs of a graph is such a space. Therefore, the greedy search approach of  graph mining uses heuristics to focus the search toward subgraphs of interest while avoiding search in less interesting portions of the space. One such heuristic is based on the compression afforded by a subgraph; that is, how much is the graph compressed if each instance of the subgraph is replaced by a single vertex. Not only does compression focus the search, but it has also been found to prefer subgraphs of interest in a variety of domains.

Motivation and Background

Many data mining and machine learning methods focus on the attributes of entities in the domain, but the relationships between these entities also represents a significant source of information, and ultimately, knowledge. Mining this relational...

This is a preview of subscription content, log in to check access.


  1. Cook, D., & Holder, L. (March/April 2000). Graph-based data mining. IEEE Intelligent Systems, 15(2), 32–41.Google Scholar
  2. Cook, D., & Holder, L. (Eds.). (2007). Mining graph data. New Jersey: Wiley.MATHGoogle Scholar
  3. Cook, D., Holder, L., Su, S., Maglothin, R., & Jonyer, I. (July/August 2001). Structural mining of molecular biology data. IEEEEngineering in Medicine and Biology, Special Issue on Genomics and Bioinformatics, 20(4), 67–74.Google Scholar
  4. Eberle, W., & Holder, L. (2006). Detecting anomalies in cargo shipments using graph properties. In Proceedings of the IEEE intelligence and security informatics conference, San Diego, CA, May 2006.Google Scholar
  5. Gonzalez, J., Holder, L., & Cook D. (2002). Graph-based relational concept learning. In: Proceedings of the nineteenth international conference on machine learning, Sydney, Australia, July 2002.Google Scholar
  6. Holder, L., & Cook, D. (July 2003). Graph-based relational learning: Current and future directions. ACM SIGKDD Explorations, 5(1), 90–93.CrossRefGoogle Scholar
  7. Holder, L., Cook, D., Coble, J., & Mukherjee, M. (March 2005). Graph-based relational learning with application to security. Fundamenta Informaticae, Special Issue on Mining Graphs, Trees and Sequences, 66(1–2), 83–101.MathSciNetMATHGoogle Scholar
  8. Jonyer, I., Cook, D., & Holder, L. (October 2001). Graph-based hierarchical conceptual clustering. Journal of Machine Learning Research, 2, 19–43.CrossRefGoogle Scholar
  9. Kukluk, J., Holder, L., & Cook, D. (2007). Inference of node replacement graph grammars. Intelligent Data Analysis, 11(4), 377–400.Google Scholar
  10. Kuramochi, M., & Karypis, G. (2001). Frequent subgraph discovery. In Proceedings of the IEEE international conference on data mining (ICDM) (pp. 313–320), San Jose, CA.Google Scholar
  11. Matsuda, T., Motoda, H., Yoshida, T., & Washio, T. (2002). Mining patterns from structured data by beam-wise graph-based induction. In Proceedings of the fifth international conference on discovery science (pp. 323–338), Lubeck, Germany.Google Scholar
  12. Nijssen, S., & Kok, J. N. (2004). A quickstart in frequent structure mining can make a difference. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD) (pp. 647–652), Seattle, WA.Google Scholar
  13. Rissanen, J. (1989). Stochastic complexity in statistical inquiry. New Jersey: World Scientific.MATHGoogle Scholar
  14. Washio, T., & Motoda H. (July 2003). State of the art of graph-based data mining. ACM SIGKDD Explorations, 5(1), 59–68.CrossRefGoogle Scholar
  15. Yan, X., & Han, J. (2002). gSpan: Graph-based substructure pattern mining. In Proceedings of the IEEE international conference on data mining (ICDM) (pp. 721–724), Maebashi City, Japan.Google Scholar
  16. Yoshida, K., Motoda, H., & Indurkhya, N. (1994). Graph-based induction as a unified learning framework. Journal of Applied Intelligence, 4, 297–328.CrossRefGoogle Scholar
  17. You, C., Holder, L., & Cook, D. (2006). Application of graph-based data mining to metabolic pathways. In Workshop on data mining in bioinformatics, IEEE international conference on data mining, Hong Kong, China, December 2006.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Lawrence Holder
    • 1
  1. 1.Washington State UniversityPullmanUSA