Clustering with Set Outliers and Applications in Relational Clustering
Summary: Defines k-center clustering with set outliers—can discard up to z candidate subsets H to model structured noise (faulty sources, corrupted join tuples). Presents first tri-criteria approximations (≤2k centers, ≤2fz sets, constant-factor cost), near-linear geometric algorithms, coresets for f=1, hardness barrier and applications to relational clustering (join-result and input-tuple outliers). (summarized by gpt-5-mini on Feb 11 2026)
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