Clustering and classification of web documents using a graph model

In this chapter we provide a summary of our previous work concerning the application of traditional machine learning techniques to data represented by graphs. We show how the fc-means clustering algorithm and the A;-nearest neighbors classification algorithm can easily and intuitively be extended from dealing with vector representations to graph representations. We present some of our experimental results, which confirm that the addition of structural information, not present in vector representations, improves both clustering and classification performance when dealing with web documents.