Improving the performance of k-means clustering through computation skipping and data locality optimizations

Abstract

We present three different optimization techniques for k-means clustering algorithm to improve the running time without decreasing the accuracy of the cluster centers significantly. Our first optimization restructures loops to improve cache behavior when executing on multicore architectures. The remaining two optimizations skip select points to reduce execution latency. Our sensitivity analysis suggests that the performance can be enhanced through a good understanding of the data and careful configuration of the parameters.