Scalable and Distributed Clustering via Lightweight Coresets
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Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of coresets called lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct light-weight coresets for k-Means clustering, Bregman clustering and maximum likelihood estimation of Gaussian mixture models. The algorithm is substantially faster than existing constructions, embarrassingly parallel and resulting coresets are smaller. In an extensive experimental evaluation, we demonstrate that the proposed method outperforms existing coreset constructions.
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