A fast clustering algorithm is presented as an alternative to the K-me
ans algorithm. By encoding training vectors selectively and changing t
he codebook updating step, the algorithm reduces the computation time.
Simulations show that the algorithm outperforms the K-means algorithm
in computation time and performance in terms of mean-squared-error.