Abstract
In this paper we describe clustering of web documents represented by graphs rather than vectors. We present a novel method for clustering graph-based data using the standard k-means algorithm and compare its performance to the conventional vector-model approach using cosine similarity. The proposed method is evaluated when using five different graph representations under two different clustering performance indices. The experiments are performed on two separate web document collections.
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© 2003 Springer-Verlag Berlin Heidelberg
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Schenker, A., Last, M., Bunke, H., Kandel, A. (2003). Graph Representations for Web Document Clustering. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_108
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DOI: https://doi.org/10.1007/978-3-540-44871-6_108
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