{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005941855,0.0007262581,0.001534635,0.001717494,0.0017495,0.002184779,0.00389689,0.002796012,0.002965267],"category_scores_gemma":[0.01351162,0.0005508951,0.001317619,0.001874006,0.001911283,0.004510636,0.003332874,0.00290831,0.001159616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001756322,"about_ca_system_score_gemma":0.001636364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002963342,"about_ca_topic_score_gemma":0.00225856,"domain_scores_codex":[0.9960146,0.001149241,0.0003069761,0.0008140851,0.001442682,0.0002724946],"domain_scores_gemma":[0.9955099,0.001512424,0.000256591,0.001473223,0.00109399,0.0001540052],"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":[0.0007345283,0.0001201447,0.001350084,0.0002903145,0.0001085352,0.0002219477,0.001148358,0.2330012,0.04349452,0.3314985,0.003186314,0.3848456],"study_design_scores_gemma":[0.00006008782,0.0001274777,0.0003856999,0.00004150766,0.00003726521,0.0001404802,0.0000397315,0.9227709,0.01085879,0.0586371,0.006826804,0.00007422113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004708732,0.0001620113,0.9941595,0.0001228363,0.00006553494,0.00005686947,0.00002337991,0.000168768,0.0005324732],"genre_scores_gemma":[0.1049146,0.0002037197,0.8926529,0.0001139592,0.00009208445,0.0001129723,0.0000919993,0.00007108131,0.001746747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005941855,"threshold_uncertainty_score":0.03142393,"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."}}