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Poster
A Better k-means++ Algorithm via Local Search
Silvio Lattanzi · Christian Sohler
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.
Author Information
Silvio Lattanzi (Google Zurich)
Christian Sohler (Google)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: A Better k-means++ Algorithm via Local Search »
Thu. Jun 13th 07:00 -- 07:05 PM Room Room 103
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