{"id":"W1986219860","doi":"10.1137/050641983","title":"Approximating K‐means‐type Clustering via Semidefinite Programming","year":2007,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Rounding; Semidefinite programming; Cluster analysis; Mathematics; Biclustering; Mathematical optimization; Linear programming; Matrix (chemical analysis); Spectral clustering; Algorithm; Computer science; Correlation clustering; CURE data clustering algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.000451649,0.0001578438,0.0001465534,0.0002126315,0.0001581243,0.0001287788,0.0001066934,0.00009127104,0.00002257175],"category_scores_gemma":[0.00004959308,0.0001557181,0.00005218574,0.0002827838,0.00001423225,0.0002017152,0.00001984573,0.0003481169,0.000008584013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015322,"about_ca_system_score_gemma":0.000008465349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000127603,"about_ca_topic_score_gemma":0.000001389147,"domain_scores_codex":[0.9989832,0.00002205412,0.0003755782,0.0001123401,0.0002079164,0.0002989077],"domain_scores_gemma":[0.9994925,0.00004906758,0.0001140806,0.000133374,0.0001209998,0.00008995809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001872174,0.0000132891,0.00004865573,0.00001155426,0.00001917669,0.0000277191,0.0001301902,0.9432331,0.001666826,0.00003857821,0.0001223523,0.05466989],"study_design_scores_gemma":[0.0001810203,0.00009566195,0.00003316327,0.000225455,0.00001251227,0.0002154108,0.00005308174,0.9901484,0.007182414,0.0001208802,0.001532976,0.0001990745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007537108,0.0001550155,0.9857324,0.00002041536,0.0004978512,0.0001155086,2.222047e-7,0.0006031387,0.005338382],"genre_scores_gemma":[0.5719867,0.0001162651,0.4274237,0.00007948034,0.0003034257,0.000001503969,0.000006753017,0.00005699031,0.0000252047],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5644496,"threshold_uncertainty_score":0.6350001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519857273548734,"score_gpt":0.2395324510064849,"score_spread":0.2243338782709975,"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."}}