Determination of usenet news groups by fuzzy inference and kohonen network

Abstract

In this work, we present a service determining user's preferred news groups among various ones. For this end, candidate terms from example documents of each news group are extracted and a number of representative keywords among them are chosen through fuzzy inference. They are then presented to Kohonen network for learning representative keywords of each news group. From the observation of training patterns, we could find the sparseness problem that lots of keywords in training patterns are empty. Thus, a method to train neural network through reduction of unnecessary dimensions by the statistical coefficient of determination is used in this paper. Experimental results show that the method is superior to the method using every input dimension in terms of cluster overlap defined by using within-cluster distance and between-clusters distance. © Springer-Verlag Berlin Heidelberg 2004.