{"id":"W1991213875","doi":"10.1016/j.neucom.2013.08.006","title":"Agreement-based fuzzy C-means for clustering data with blocks of features","year":2013,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Technology Futures","keywords":"Computer science; Data mining; Fuzzy clustering; Cluster analysis; Fuzzy logic; Artificial intelligence; Pattern recognition (psychology)","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":[0.0002224237,0.0001533693,0.0002282737,0.00008988685,0.0001845632,0.000193959,0.001317789,0.00003088005,0.00001013994],"category_scores_gemma":[0.00004015302,0.0001228928,0.00006144289,0.0003136848,0.00003223184,0.0003548723,0.0006617801,0.0001028294,0.000003293896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008625412,"about_ca_system_score_gemma":0.00003499325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001144386,"about_ca_topic_score_gemma":0.0000384826,"domain_scores_codex":[0.9986047,0.00002718317,0.0002959706,0.0005103548,0.0002303862,0.0003314616],"domain_scores_gemma":[0.9984594,0.0001840542,0.0002471258,0.0009080754,0.0001352177,0.00006615564],"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":[0.00004002343,0.0001880617,0.004628102,0.0003461936,0.0001646478,0.00001433946,0.0007845313,0.2440914,0.009855327,0.001742396,0.004716284,0.7334287],"study_design_scores_gemma":[0.0003796935,0.0001651338,0.001821907,0.00006481986,0.00001793396,0.000008583981,0.00004137811,0.9953781,0.0009108277,0.00005304576,0.001011067,0.0001474657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03378814,0.00003747777,0.9638827,0.000513594,0.0001006342,0.000362577,0.000005816793,0.0000934251,0.001215591],"genre_scores_gemma":[0.7242488,4.263368e-7,0.2753158,0.0002376362,0.00009056874,0.000007938244,0.000012044,0.00001265331,0.00007417054],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7512867,"threshold_uncertainty_score":0.5011423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02532579358717683,"score_gpt":0.2401143303935832,"score_spread":0.2147885368064064,"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."}}