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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
6936
Venue
SIGMOD
Year
2024
Pagerank
4.1945683e-05
Overall Rank
10,971 | 23.68%
DOI
10.1145/3654976

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

Rank Cited Paper Year Venue Pagerank
33 BIRCH: An Efficient Data Clustering Method for Very Large Databases 1996 SIGMOD 0.00077324389
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