Smoothed Analysis of the k-Means Method
David Arthur, Bodo Manthey, Heiko Röglin · Journal of the ACM · 2011
The k -means method is one of the most widely used clustering algorithms, drawing its popularity from its speed in practice. Recently, however, it was shown to have exponential worst-case running time. In order to close the gap between practical performance and theoretical analysis, the k -means method has been studied in the model of smoothed analysis. But even the smoothed analyses so far are unsatisfactory as the bounds are still super-polynomial in the number n of data points. In this article, we settle the smoothed running time of the k -means method. We show that the smoothed number of iterations is bounded by a polynomial in n and 1/ σ , where σ is the standard deviation of the Gaussian perturbations. This means that if an arbitrary input data set is randomly perturbed, then the k -means method will run in expected polynomial time on that input set.