Classifying Web Pages by Genre - A Distance Function Approach

The research reported in this paper is part of a larger project on the automatic classification of Web pages by their genres, using a distance function classification model. In this paper, we investigate the effect of several commonly used data preprocessing steps, explore the use of byte and word n-grams, and test our classification model on three Web page data sets. Our approach is to represent each Web page by a profile that is composed of fixed-length n-grams and their normalized frequencies within the document. Similarly, each of the genres in a data set is represented by a profile that is constructed by combining the n-gram profiles for each exemplar Web page of that genre, forming a centroid profile for each Web page genre. We use a distance function approach to determine the similarity between two profiles, assigning each Web page the label of the genre profile to which its profile is most similar. Our results compare very favorably to those of other researchers.