Abstract
Graph based data representation offers a convenient possibility to represent entities, their attributes, and their relationships to other entities. Consequently, the use of graph based representation for data mining has become a promising approach to extracting novel and useful knowledge from relational data. In order to check whether a certain graph occurs, as a substructure, within a larger database graph, the widely studied concept of subgraph isomorphism can be used. However, this conventional approach is rather limited. In the present paper the concept of subgraph isomorphism is substantially extended such that it can cope with don’t care symbols, variables, and constraints. Our novel approach leads to a powerful graph matching methodology which can be used for advanced graph based data mining.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Perner, P., Rosenfeld, A.: MLDM 2003. LNCS, vol. 2734. Springer, Heidelberg (2003)
Perner, P., Imiya, A. (eds.): MLDM 2005. LNCS (LNAI), vol. 3587. Springer, Heidelberg (2005)
Perner, P. (ed.): ICDM 2006. LNCS (LNAI), vol. 4065. Springer, Heidelberg (2006)
Perner, P. (ed.): MLDM 2007. LNCS (LNAI), vol. 4571. Springer, Heidelberg (2007)
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. Journal of Pattern Recognition and Artificial Intelligence 18(3), 265–298 (2004)
Kandel, A., Bunke, H., Last, M. (eds.): Applied Graph Theory in Computer Vision and Pattern Recognition. Studies in Computational Intelligence, vol. 52. Springer, Heidelberg (2007)
Cook, D., Holder, L. (eds.): Mining Graph Data. Wiley-Interscience, Chichester (2007)
Blau, H., Immerman, N., Jensen, D.: A visual query language for relational knowledge discovery. Technical report, University of Massachusetts (2001)
Marcus, S., Moy, M., Coffman, T.: Social Network Analysis. In: Cook, D., Holder, L. (eds.) Mining Graph Data, pp. 443–467. Wiley-Interscience, Chichester (2007)
Ullman, J.: An algorithm for subgraph isomorphism. Journal of the Association for Computing Machinery 23(1), 31–42 (1976)
Cordella, L., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(20), 1367–1372 (2004)
Larrosa, J., Valiente, G.: Constraint satisfaction algorithms for graph pattern matching. Mathematical Structures in Computer Science 12(4), 403–422 (2002)
Klimt, B., Yang, Y.: Introducing the Enron corpus. In: Proc. First Conference on Email and Anti-Spam, CEAS (Electronic Proceedings)(2004)
DTP, D.T.P.: Aids antiviral screen (2004), http://dtp.nci.nih.gov/docs/aids/aids_data.html
The Internet Movie Database, http://www.imdb.com
Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recognition Letters 1, 245–253 (1983)
Bunke, H., Dickinson, P., Kraetzl, M.: Theoretical and algorithmic framework for hypergraph matching. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 463–470. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brügger, A., Bunke, H., Dickinson, P., Riesen, K. (2008). Generalized Graph Matching for Data Mining and Information Retrieval. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-70720-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70717-2
Online ISBN: 978-3-540-70720-2
eBook Packages: Computer ScienceComputer Science (R0)