A Better k-means++ Algorithm via Local Search
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3662-3671, 2019.
In this paper, we develop a new variant of k-means++ seeding that in expectation achieves a constant approximation guarantee. We obtain this result by a simple combination of k-means++ sampling with a local search strategy. We evaluate our algorithm empirically and show that it also improves the quality of a solution in practice.