Improved Guarantees for k-means++ and k-means++ Parallel
Abstract
In this paper, we study k-means++ and k-means||, 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||. Our results give a better theoretical justification for why these algorithms perform extremely well in practice.
Type
Publication
In Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
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Aravind Reddy
Postdoctoral Associate
My research interests include machine learning and computational biology.