Web Categorisation Using Distance-Based Decision Trees

In Web classification, web pages are assigned to pre-defined categories mainly according to their content (content mining). However, the structure of the web site might provide extra information about their category (structure mining). Traditionally, both approaches have been applied separately, or are dealt with techniques that do not generate a model, such as Bayesian techniques. Unfortunately, in some classification contexts, a comprehensible model becomes crucial. Thus, it would be interesting to apply rule-based techniques (rule learning, decision tree learning) for the web categorisation task. In this paper we outline how our general-purpose learning algorithm, the so called distance based decision tree learning algorithm (DBDT), could be used in web categorisation scenarios. This algorithm differs from traditional ones in the sense that the splitting criterion is defined by means of metric conditions (“is nearer than”). This change allows decision trees to handle structured attributes (lists, graphs, sets, etc.) along with the well-known nominal and numerical attributes. Generally speaking, these structured attributes will be employed to represent the content and the structure of the web-site