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A Better k-means++ Algorithm via Local Search

Christian Alexander Sohler
ICML 2019
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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.