In this paper we describe work relating to classification of Web documents using a graph-based model instead of the traditional vector-based model for document representation. We compare the classification accuracy of the vector model approach using the k-nearest neighbor (k-NN) algorithm to a novel approach which allows the use of graphs for document representation in the k-NN algorithm. The proposed method is evaluated on three different Web document collections using the leave-one-out approach for measuring classification accuracy. The results show that the graph-based k-NN approach can outperform traditional vector-based k-NN methods in terms of both accuracy and execution time.


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    Title :

    Classification of Web documents using a graph model


    Contributors:
    Schenker, A. (author) / Last, M. (author) / Bunke, H. (author) / Kandel, A. (author)


    Publication date :

    2003-01-01


    Size :

    264185 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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