A competitive learning algorithm using symmetry

In this paper, we propose a new competitive learning algorithm for training single-layer neural networks to cluster data. The proposed algorithm adopt s a new measure based on the idea of "symmetry" so that neurons compete wit h each other based on the symmetrical distance instead of the Euclidean dis tance. The detected clusters may be a set of clusters of different geometri cal structures. Four data sets are tested to illustrate the effectiveness o f our proposed algorithm.