Decision Tree Structures for Graph Database Filtering

Abstract

In structural pattern recognition it is often required to match an unknown sample against a database of candidate patterns in order to find the most similar prototype. If the patterns are represented using graphs, the sample's graph is matched against a database of model graphs and the pattern recognition problem is turned into a graph matching problem. Graph matching is a powerful yet computationally expensive procedure. If the unknown sample is matched against a whole database of prototypes, the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor an approach based on machine learning techniques is proposed in this paper. The graphs are represented using feature vectors. Based on these vectors a decision tree is built to index the database. The decision tree allows at runtime to eliminate a number of graphs from the database as possible matching candidates. Experimental results are reported demonstrating the efficiency of the proposed filtering procedure. The work presented in this paper extends previous studies from the case of graph-isomorphism to the problem of subgraph-isomorphism. © Springer-Verlag 2004.