The effect of cluster location and dataset size on 2-stage k-means algorithm

Abstract

Paper introduces the 2-stage k-means algorithm which is faster than the standard 1-stage k-means algorithm. The main idea of the 2-stages is to move, in the first stage (fast), the centers of the clusters closer to their final locations. This will be done by using a small part of the data to achieve faster calculation. The next stage (slow) stage will start from the centers found during the first stage (fast). Different initial locations of the clusters have been used while testing the algorithms here. With bigger datasets, it is shown that the 2-stage clustering method achieves better speed-up.