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)
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| 32 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00050802843 |
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