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In this paper, we study the Bregman k-means problem with respect to-similar Bregman divergences (BKMP). Given an n-point set and BKMP is to find a center subset with and separate the given set into k clusters accordingly, aiming to minimize the sum of similar Bregman divergences of the points in to their nearest centers. We propose a new variant of k-means++ by employing the local search scheme, and show the algorithm deserves a constant approximation guarantee. © 2020, Springer Nature Switzerland AG.
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