{"id":"W3037695460","doi":"10.4230/lipics.icalp.2020.85","title":"Proportionally Fair Clustering Revisited","year":2020,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Metric space; Mathematics; Centroid; Combinatorics; Metric (unit); Generalization; Discrete mathematics; Cluster (spacecraft); Approximation algorithm; k-medians clustering; Correlation clustering; Computer science; CURE data clustering algorithm; Statistics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004914843,0.001161456,0.001821777,0.001114822,0.003121925,0.002978511,0.004432275,0.00311694,0.006324113],"category_scores_gemma":[0.01725073,0.0005642354,0.001295165,0.002236614,0.003639172,0.007310608,0.003625576,0.003803462,0.001133228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004931226,"about_ca_system_score_gemma":0.002801156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005329821,"about_ca_topic_score_gemma":0.003166778,"domain_scores_codex":[0.9945168,0.001712559,0.000171319,0.00144681,0.001485997,0.0006665598],"domain_scores_gemma":[0.9926041,0.004047407,0.0003976685,0.001670354,0.0008867771,0.0003937106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001839692,0.00009661648,0.0007052392,0.0001456015,0.00007370656,0.0001446133,0.0004013891,0.2093722,0.00127838,0.7260173,0.007329776,0.05425123],"study_design_scores_gemma":[0.00004085439,0.0000668856,0.0002048403,0.00003254026,0.00002730411,0.0002049002,0.0001055103,0.5327991,0.001631508,0.4528675,0.01198112,0.00003780771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01790575,0.002205377,0.956822,0.002369606,0.0005307914,0.000141671,0.0001142905,0.0003124232,0.01959801],"genre_scores_gemma":[0.7546905,0.002070896,0.2172295,0.001422407,0.001085031,0.0002253377,0.0002233537,0.0003311804,0.02272186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006324113,"threshold_uncertainty_score":0.0357787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03339787288379148,"score_gpt":0.2676198671543172,"score_spread":0.2342219942705257,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}