Worst-Case and Smoothed Analysis of the k-Means Method with Bregman Divergences

Abstract

The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice despite its exponential worst-case running-time. To narrow the gap between theory and practice, k-means has been studied in the semi-random input model of smoothed analysis, which often leads to more realistic conclusions than mere… (More)

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