Web Documents Categorization using Fuzzy Representation and HAC

Most of the existing techniques for characterization of Web documents are based on term-frequent), analysis. In such models, given a set of documents, the characterization of each document is represented by a feature vector in a vector space. Howevel; as Web documents written in HTML are semi-structured documents by means of tags, the traditional techniques that assign term weights only by the frequency of occurrence may not be able to provide satisfactory results in representing the contents of such documents. Some recent studies have shown that the f u u y representation (FR) of WWW information based on SigniJicance of HTML tag is an effective alternative fo r characterizing Web documents. In this papel; the FR to generate the feature vector for each Web document and the Hierarchical Agglomerative Clustering (HAC) algorithm are applied to investigate the efficiency and tfectiveness fo r automatic categorization of Web documents with similar contents. Experiments conducted suggest several benefits of using such an approach.