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Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially

Summary: Coreset-based 2-round MapReduce for k-center with/without outliers; 1-pass streaming for the outlier variant. Achieves epsilon-additive approximation to best sequential k-center; space-efficient for small doubling dimension, empirical scalability. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12191
Venue
VLDB
Year
2019
Pagerank
6.7407122e-05
Overall Rank
4,366 | 70.05%
DOI
10.14778/3317315.3317319

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Authors

BibTeX Citation

@article{ceccarello_vldb19,
        title = {{Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially}},
        author = {Ceccarello, Matteo and Pietracaprina, Andrea and Pucci, Geppino},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {7},
        pages = {766--778},
        doi = {10.14778/3317315.3317319},
        url = {https://doi.org/10.14778/3317315.3317319},
        year = {2019}
}

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