{"id":"W6966854856","doi":"10.48448/tsda-8543","title":"Scalable and Globally Optimal Generalized L_1 k-center Clustering via Constraint Generation in Mixed Integer Linear Programming","year":2023,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Scalability; Benchmark (surveying); Linear programming; Constrained clustering; Integer programming; CURE data clustering algorithm; Correlation clustering; Constraint (computer-aided design); Outlier","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002188923,0.0006333935,0.0006471716,0.001517063,0.0002354047,0.0005747812,0.0007709563,0.0003780086,0.0002845066],"category_scores_gemma":[0.0002612481,0.0006047951,0.00007798064,0.001919669,0.001898092,0.0004572319,0.0007185612,0.0004968976,0.0006448678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007272332,"about_ca_system_score_gemma":0.0005427334,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001558024,"about_ca_topic_score_gemma":0.02105931,"domain_scores_codex":[0.9953005,0.0001357112,0.0007616957,0.001466179,0.001139377,0.001196501],"domain_scores_gemma":[0.9984547,0.00003132393,0.0004038612,0.0005731529,0.0002072827,0.0003296096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004351745,0.002557775,0.00932618,0.000965033,0.000517318,0.00106409,0.002480306,0.1134153,0.2052652,0.005895121,0.1296091,0.5284693],"study_design_scores_gemma":[0.002097692,0.0001170731,0.00007709307,0.0004454916,0.00003672271,0.0001008333,0.0001672727,0.9347045,0.0003454571,0.00002359964,0.06103292,0.0008512873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1209489,0.004120925,0.6697637,0.002400226,0.01732322,0.02083581,0.002415956,0.01832724,0.143864],"genre_scores_gemma":[0.1132738,0.0003052258,0.8140711,0.0006250831,0.002960399,0.0003967121,0.00102379,0.004754947,0.06258895],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8212892,"threshold_uncertainty_score":0.9996403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536832334209291,"score_gpt":0.3031033283615216,"score_spread":0.2677350050194287,"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."}}