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- W3115752107 abstract "$k$-means++ cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance. Despite its wide adoption, $k$-means++ sometimes suffers from being slow on large data-sets so a natural question has been to obtain more efficient algorithms with similar guarantees. In this paper, we present a near linear time algorithm for $k$-means++ seeding. Interestingly our algorithm obtains the same theoretical guarantees as $k$-means++ and significantly improves earlier results on fast $k$-means++ seeding. Moreover, we show empirically that our algorithm is significantly faster than $k$-means++ and obtains solutions of equivalent quality." @default.
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- W3115752107 date "2020-12-22" @default.
- W3115752107 modified "2023-10-01" @default.
- W3115752107 title "Fast and Accurate $k$-means++ via Rejection Sampling" @default.
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