Best of both

Abstract

Although each iteration of the popular k-Means clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to a solution. This paper introduces an enhancement of k-Means in which local search is used to accelerate convergence without greatly increasing the average computational cost of the iterations. The local search involves a carefully-controlled number of swap operations resembling those of the more robust k-Medoids clustering heuristic. We show empirically that the proposed method improves convergence results when compared to standard k-Means.