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Graph distances using graph union
Pattern Recognition Letters
A graph distance metric combining maximum common subgraph and minimum common supergraph
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Machine Learning
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Information Processing and Management: an International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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In this paper we describe work relating toclassification of web documents using a graph-basedmodel instead of the traditional vector-based model fordocument representation. We compare the classificationaccuracy of the vector model approach using the k-Nearest Neighbor (k-NN) algorithm to a novel approachwhich allows the use of graphs for documentrepresentation in the k-NN algorithm. The proposedmethod is evaluated on three different web documentcollections using the leave-one-out approach formeasuring classification accuracy. The results show thatthe graph-based k-NN approach can outperformtraditional vector-based k-NN methods in terms of bothaccuracy and execution time.