Generalized Graph Matching for Data Mining and Information Retrieval

Authors:
Alexandra Brügger;Horst Bunke;Peter Dickinson;Kaspar Riesen
Affiliations:
Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland CH-3012;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland CH-3012;C3I Division, DSTO, Edinburgh, Australia SA 5111;Institute of Computer Science and Applied Mathematics, University of Bern, Bern, Switzerland CH-3012
Venue:
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Year:
2008

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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.