An empirical evaluation of different initializations on the number of K-Means iterations

Abstract

This paper presents an analysis of the number of iterations K-Means takes to converge under different initializations. We have experimented with seven initialization algorithms in a total of 37 real and synthetic datasets. We have found that hierarchical-based initializations tend to be most effective at reducing the number of iterations, especially a divisive algorithm using the Ward criterion when applied to real datasets. © 2013 Springer-Verlag.