{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002252347,0.00009740498,0.0001776754,0.00006808055,0.00007300929,0.00003837151,0.0006018743,0.00004615466,0.00004349391],"category_scores_gemma":[0.0001492004,0.00007947376,0.00005288864,0.001434697,0.0001128077,0.0002752552,0.0003957998,0.0001645248,0.000009949947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000531318,"about_ca_system_score_gemma":0.0001388522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000319267,"about_ca_topic_score_gemma":0.00007009373,"domain_scores_codex":[0.9986638,0.0001118998,0.0004198152,0.0003052044,0.0002820803,0.0002172201],"domain_scores_gemma":[0.9986002,0.0001695646,0.0001288677,0.0007420863,0.000319867,0.00003945386],"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":[0.00001250437,0.0003971597,0.005816538,0.0001956814,0.00002086376,0.00004130409,0.005859905,0.7435014,0.009583665,0.004473135,0.00001805804,0.2300798],"study_design_scores_gemma":[0.00004456182,0.00001913964,0.0006290266,0.0001526615,0.000001926698,0.00004888294,0.0003177871,0.8126771,0.185354,0.00059686,0.00008270228,0.00007533822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03009083,0.00005190042,0.9681724,0.0003819221,0.000130232,0.0001502469,0.000004500384,0.00001761657,0.001000382],"genre_scores_gemma":[0.7801068,0.00003817465,0.2189187,0.00002275049,0.00002039764,0.00001921946,0.000002442408,0.00001016319,0.0008613407],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7500159,"threshold_uncertainty_score":0.3240846,"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."}}