A Graph-Based Framework for Web Document Mining

In this paper we describe methods of performing data mining on web documents, where the web document content is represented by graphs. We show how traditional clustering and classification methods, which usually operate on vector representations of data, can be extended to work with graph-based data. Specifically, we give graph-theoretic extensions of the k-Nearest Neighbors classification algorithm and the k-means clustering algorithm that process graphs, and show how the retention of structural information can lead to improved performance over the case of the vector model approach. We introduce several different types of web document representations that utilize graphs and compare their performance for clustering and classification.

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