A Constant-Factor Bi-Criteria Approximation Guarantee for $k$-means++

Abstract

This paper studies the $k$-means++ algorithm for clustering as well as the class of $D^{\ell }$ sampling algorithms to which $k$-means++ belongs. It is shown that for any constant factor $\beta >1$, selecting $\beta k$ cluster centers by $D^{\ell }$ sampling yields a constant-factor approximation to the optimal clustering with $k$ centers, in expectation and without conditions on the dataset. This result extends the previously known $O(\log k)$ guarantee for the case $\beta =1$ to the constant-factor bi-criteria regime. It also improves upon an existing constant-factor bi-criteria result that holds only with constant probability.

17 pages, 1 figure