{"id":"W3182227316","doi":"10.18280/ria.350302","title":"Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; k-medoids; Minification; Algorithm; Medoid; k-means clustering; Mathematics; Computer science; Mathematical optimization; Correlation clustering; CURE data clustering algorithm; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008226012,0.001689674,0.002017298,0.001923339,0.001951497,0.002073567,0.002304317,0.002215245,0.001381056],"category_scores_gemma":[0.02685102,0.001176007,0.001546945,0.001806632,0.001902607,0.002876523,0.00258168,0.002265626,0.00103999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272537,"about_ca_system_score_gemma":0.00304634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003695447,"about_ca_topic_score_gemma":0.003174992,"domain_scores_codex":[0.9925501,0.003537106,0.000715163,0.001084239,0.001796606,0.000316746],"domain_scores_gemma":[0.9870278,0.007907729,0.0009540938,0.0008903527,0.00291445,0.0003056061],"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":[0.0008277597,0.0001813432,0.004188348,0.0007399458,0.0003071984,0.0001679891,0.001047321,0.7575698,0.01054624,0.02305104,0.003294686,0.1980783],"study_design_scores_gemma":[0.00004170909,0.0001486792,0.0005447307,0.00004647026,0.00004537789,0.000147075,0.00016133,0.9789725,0.008707969,0.008875032,0.002265808,0.00004327359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008167668,0.0002489118,0.9903997,0.00009979179,0.0000311583,0.0000738392,0.00002736562,0.0003276068,0.0006239087],"genre_scores_gemma":[0.1420259,0.0003168962,0.8555003,0.0001199837,0.00004499012,0.0003727553,0.0002482924,0.0002636066,0.001107125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008226012,"threshold_uncertainty_score":0.04350382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870689876319596,"score_gpt":0.3027406127607996,"score_spread":0.2740337139976036,"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."}}