{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002451526,0.001623138,0.001390661,0.0008317751,0.0006505387,0.001381533,0.001506627,0.001549524,0.002527519],"category_scores_gemma":[0.006623043,0.0009372588,0.001065689,0.001246791,0.001517649,0.001615227,0.001446249,0.003024476,0.0007915326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001452688,"about_ca_system_score_gemma":0.001713842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004548074,"about_ca_topic_score_gemma":0.005756875,"domain_scores_codex":[0.9982007,0.0009150529,0.00006655527,0.0002785907,0.000410092,0.0001290595],"domain_scores_gemma":[0.9960397,0.002896786,0.0002894277,0.000233668,0.0004344982,0.000105794],"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":[0.00004728554,0.00005755394,0.0002146534,0.00008918329,0.00002282945,0.00003589242,0.00005251217,0.9609562,0.000998926,0.02078453,0.002230201,0.01451029],"study_design_scores_gemma":[0.000004636051,0.000008151864,0.00001832819,0.000003103233,0.000001176081,0.000006179672,0.000007986792,0.9940802,0.0001942581,0.005498393,0.0001747389,0.000002826037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006136537,0.00007875232,0.9917807,0.0001821078,0.00001581538,0.0000354105,0.00007832845,0.0002367709,0.001455433],"genre_scores_gemma":[0.2696442,0.0003670396,0.7244222,0.0003261922,0.00006410952,0.0004541636,0.0008082787,0.0003015735,0.003612131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004548074,"threshold_uncertainty_score":0.01296508,"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."}}