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


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)


Figures and Tables

Sorry, we couldn't extract any figures or tables for this paper.

Slides referencing similar topics