Improved Guarantees for k-means++ and k-means++ Parallel
Konstantin Makarychev, A. Rama Mohan Reddy, Liren Shan · arXiv (Cornell University) · 2020
In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved approximation and bi-criteria approximation guarantees for k-means++ and k-means++ parallel. Our results give a better theoretical justification for why these algorithms perform extremely well in practice. We also propose a new variant of k-means++ parallel algorithm (Exponential Race k-means++) that has the same approximation guarantees as k-means++.