Fast and Accurate k-means++ via Rejection Sampling

by   Vincent Cohen-Addad, et al.

k-means++ <cit.> 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.


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