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 predefined set of clusters which, in the first case, is used to define the function that best discriminates among clusters and, in the second case, is used to train the neural network.

We classify the Internet users based on a data set measured at the access network of a Portuguese ISP. Using Cluster Analysis performed over the first half of users 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 that Discriminant Analysis outperforms Neural Networks as a classification procedure.