A Better k-means++ Algorithm via Local Search
Silvio Lattanzi · Christian Sohler
2019 Oral
Abstract
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.
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