High performance clustering using dynamic competitive learning

A new learning algorithm which adjusts the weight vectors of an artificial neural network in a dynamic manner is compared with the winner-take-all and frequency sensitive competitive learning algorithms. Using computer simulation, the method is shown to outperform the other methods in terms of efficient use of weight vectors, insensitivity to initial weight values, and giving low clustering distortion.