Classification of Internet users using discriminant analysis and neural networks

Abstract

The (reliable) classification of Internet users, based on their hourly traffic profile, can be advantageous in several traffic engineering tasks and in the selection of suitable tariffing plans. For example, it can be used to optimize the routing by mixing users with contrasting hourly traffic profiles in the same network resources or to advise users on the tariffing plan that best suits their needs. In this paper we compare the use of discriminant analysis and artificial neural networks for the classification of Internet users. The classification is based on a partition obtained by cluster analysis. We classify the Internet users based on a data set measured at the access network of a Portuguese ISP. Using cluster analysis performed over half of the users (randomly chosen) we have identified three groups of users with similar behavior. The classification methods were applied to the second half of users and the obtained classification results compared with those of cluster analysis performed over the complete set of users. Our findings indicate both discriminant analysis and neural networks are valuable classification procedures, with the former slightly outperforming the latter, for the specific scenario under analysis.