{"id":"W2734881792","doi":"10.1137/1.9781611975031.27","title":"Approximation Schemes for Clustering with Outliers","year":2018,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Outlier; Metric (unit); Metric space; Facility location problem; Set (abstract data type); Overhead (engineering); Computer science; Mathematics; Euclidean distance; Data point; Fraction (chemistry); Integer (computer science); Algorithm; Data mining; Mathematical optimization; Discrete mathematics; Statistics; Artificial intelligence; Engineering","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":[0.0004403893,0.0003896394,0.0004275387,0.00007449784,0.0003729174,0.0002344665,0.0001704111,0.0003721989,0.00008244596],"category_scores_gemma":[0.00001861383,0.0003347243,0.0002764901,0.00002372293,0.0001684711,0.00009710177,0.0001379343,0.0001648798,0.00002568446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004164174,"about_ca_system_score_gemma":0.00003215993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005150407,"about_ca_topic_score_gemma":0.00002263569,"domain_scores_codex":[0.9986099,4.534409e-7,0.0004554939,0.000413969,0.0002449216,0.0002752585],"domain_scores_gemma":[0.9992226,0.00003827752,0.0003051657,0.0002501706,0.0001618773,0.00002195149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001271212,0.0000306011,3.032022e-7,0.003448427,0.0004358817,5.929995e-8,0.0005437328,0.0000224219,0.00003258849,0.9440001,0.03136708,0.01999166],"study_design_scores_gemma":[0.00221404,0.00004606807,7.4327e-8,0.0002722702,0.0006263589,3.303764e-7,0.00119443,0.01224133,0.00005473208,0.1313386,0.8513816,0.0006301117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008474971,0.00002968751,0.1765582,0.0004294512,0.0006529908,0.0085821,0.0001053094,0.0003744701,0.8124203],"genre_scores_gemma":[0.004783679,0.00002670677,0.144385,0.002578537,0.01206701,0.003196205,0.001418833,0.0005835303,0.8309605],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8200145,"threshold_uncertainty_score":0.9999105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07641030250898438,"score_gpt":0.2334986998941839,"score_spread":0.1570883973851995,"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."}}