Fast vector quantizer on neural clustering networks providing globally optimal cluster solutions

Our earlier algorithm (1996) on neural clustering networks (proved to provide globally optimal cluster solutions) is improved herein for considerably faster convergence. The neural network approach and the sort of clustering (vector quantization) are explained. Then the new method is introduced. Computational results are given which demonstrate that the globally optimal solution may be reliable, obtained by the fast algorithm whose convergence rate is of the same order as that of the K-means clustering algorithm. In specific cases the new algorithm may be even faster than K-means. Latent risks of a poor performance of K-means are visualized and consequences for the potential use of vector quantization are discussed