A FAST K-MEANS TYPE CLUSTERING ALGORITHM

Abstract

This paper describes a new $k$-means type clustering algorithm which gives excellent results for a moderate computational cost. It is particularly suitable for partitioning large data sets into a number of clusters where the conventional $k$-means algorithm becomes computationally unmanageable. While it does not guarantee to reach a global optimum, its performance in practice is very good indeed, as demonstrated by theoretical analysis and experiments on color image data.