The behavior of k-Means: An empirical study
Kashif Javed, H.A. Babri, Mehreen Saeed · 2008 Second International Conference on Electrical Engineering · 2008
In this paper, we study the behavior of the typical k-Means clustering algorithm by investigating the distributions of the final centroids, the sum-of-squares error and the iterations to convergence. This behavior is observed on two different synthetic data sets. It is found that when the clusters are well isolated from each other, the spread of the solutions found by k-Means algorithm indicates a much larger number of local minima as compared to the data set in which clusters overlap.