Smoothed analysis of the k-means method


Arthur, David and Manthey, Bodo and Röglin, Heiko (2011) Smoothed analysis of the k-means method. Journal of the ACM, 58 (5). p. 19. ISSN 0004-5411

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Abstract: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/sigma, where sigma 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.
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Copyright:© 2011 IEEE
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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