Scalable and Distributed Clustering via Lightweight Coresets

@article{Bachem2017ScalableAD,
  title={Scalable and Distributed Clustering via Lightweight Coresets},
  author={Olivier Bachem and Mario Lucic and Andreas Krause},
  journal={CoRR},
  year={2017},
  volume={abs/1702.08248}
}
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… CONTINUE READING
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