Best of both: a hybridized centroid-medoid clustering heuristic

Although each iteration of the popular kMeans 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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Nizar Grira, Michael E. Houle
• Keywords: Average Computational Cost, Heuristic Scales, ICML 2007, Machine Learning, Method Improves Convergence