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A Better k-means++ Algorithm via Local Search

Silvio Lattanzi · Christian Sohler

Pacific Ballroom #193

Keywords: [ Combinatorial Optimization ] [ Clustering ]


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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