Algorithm AS 136: A K-Means Clustering Algorithm

Abstract

The K-means clustering algorithm is described indetail by Hartigan(1975). An efficient version of the algorithm is presented here.The aim of the K-means algorithm is to divide M points in N dimensions into K clusters so that the within-cluster sum of squares is minimized. It is not practical to require that the solution has minimal sum of squares against all partitions except when M,N are small and K = 2. We seek instead "local" optima, solution such that no movement of a point from one cluster to another will reduce the within cluster sum of squares.