Unsupervised clustering based on a competitive cost function

A general unsupervised learning scheme based on a competitive cost function is presented. A gradient technique is used to minimize the cost function. The algorithm is then applied to clustering problems by using a particular unit activation function. A quadratic potential function is used which permits clustering the data with ellipsoids. Comparisons are made with the dynamic clusters method on artificial data and on R.A. Fisher´s (1936) iris data set. Results show that the network is able to cluster the data and performs well compared to the dynamic clusters technique, though it fails to make optimal partition of the data for some problems. Moreover, it automatically finds the number of clusters, contrary to most clustering techniques