{"id":"W2114217619","doi":"10.1109/nnsp.1997.622414","title":"An improved scheme for the fuzzifier in fuzzy clustering","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Fuzzy clustering; Data mining; Computer science; Correlation clustering; Fuzzy set; Embedding; Constrained clustering; Scheme (mathematics); Probabilistic logic; Fuzzy logic; Artificial intelligence; CURE data clustering algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003548368,0.0001081175,0.0001022407,0.00008526711,0.0001403846,0.0001910121,0.001288191,0.00004534372,0.00003093861],"category_scores_gemma":[0.00006032274,0.0000757459,0.00003826345,0.0003457118,0.00003800787,0.0006316401,0.0003433463,0.0001660574,0.00002838624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005822649,"about_ca_system_score_gemma":0.00001219179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004657453,"about_ca_topic_score_gemma":0.0001067734,"domain_scores_codex":[0.998786,0.00003007329,0.0001741949,0.0003712261,0.0001842188,0.0004542852],"domain_scores_gemma":[0.9988176,0.0002141131,0.00002756933,0.0007985859,0.0000601061,0.00008206214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001869392,0.0001574239,0.0001451667,0.0000363421,0.00001706698,0.00001238994,0.001420038,0.007215747,0.01555117,0.005064232,0.0004675259,0.9698942],"study_design_scores_gemma":[0.0004110328,0.00008853725,0.0002781966,0.000004884129,4.874374e-7,0.000006872028,0.00006097493,0.9954714,0.0007194966,0.0007581706,0.002083939,0.0001160404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001451875,0.0001034446,0.9942025,0.001677174,0.0001720891,0.0004051205,8.52439e-7,0.0001405866,0.001846339],"genre_scores_gemma":[0.3649817,0.0000284516,0.6307563,0.0004531062,0.0001389615,0.0002152823,5.606464e-7,0.00002178719,0.00340386],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9882556,"threshold_uncertainty_score":0.3088828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04832545442196626,"score_gpt":0.3193794344841269,"score_spread":0.2710539800621606,"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."}}