Settling Time vs. Accuracy Tradeoffs for Clustering Big Data

Summary: Settles the runtime/accuracy frontier for big-data k-means/k-median: shows sensitivity-sampling coresets can be built in near-linear time, refuting the folklore superlinear barrier. Then benchmarks sampling/coreset heuristics in batch and streaming to characterize when exact-ish summaries are worth the cost vs. crude subsampling. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID: 6998
Venue: SIGMOD
Year: 2024
Pagerank: 5.093636e-05
Overall Rank: 11,184 | 23.27%
DOI: 10.1145/3654976

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Andrew Draganov (Aarhus University)
2. David Saulpic (CNRS; Université Paris Cité)
3. Chris Schwiegelshohn (Aarhus University)

BibTeX Citation

@inproceedings{draganov_sigmod24,
        title = {{Settling Time vs. Accuracy Tradeoffs for Clustering Big Data}},
        author = {Draganov, Andrew and Saulpic, David and Schwiegelshohn, Chris},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654976},
        url = {https://dl.acm.org/doi/10.1145/3654976},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
31	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00050347119

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