A Better k-means++ Algorithm via Local Search

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.