{"id":"W2008599133","doi":"10.1016/j.ijar.2004.03.002","title":"A fuzzy noise-rejection data partitioning algorithm","year":2004,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Cluster analysis; Outlier; Noise (video); Computer science; Data mining; Fuzzy clustering; CURE data clustering algorithm; Fuzzy logic; Noisy data; Algorithm; Pattern recognition (psychology); Correlation clustering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00128878,0.0008396911,0.001782525,0.001449607,0.00114014,0.001464476,0.002109906,0.001652809,0.002572003],"category_scores_gemma":[0.003539603,0.0005742772,0.0009382881,0.001636071,0.0005719125,0.00134702,0.001821969,0.001174338,0.001368565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005724006,"about_ca_system_score_gemma":0.001245056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003482686,"about_ca_topic_score_gemma":0.003093532,"domain_scores_codex":[0.998904,0.0002152878,0.00008676144,0.0002793883,0.0004196912,0.0000948498],"domain_scores_gemma":[0.9989545,0.0003485832,0.00005023077,0.0001877439,0.00040593,0.0000528625],"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":[0.0007772375,0.0001787741,0.0006535806,0.0001353893,0.0001186525,0.00007613312,0.0001205953,0.08864008,0.02699848,0.008400628,0.003926865,0.8699737],"study_design_scores_gemma":[0.00009887834,0.00009988077,0.0003262766,0.00001484649,0.00004364288,0.0001398483,0.00003468388,0.9771466,0.01213453,0.006325752,0.003611221,0.00002396956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006225704,0.0001673755,0.99257,0.00006428645,0.00004236617,0.00005002305,0.00003502858,0.0004317208,0.0004135778],"genre_scores_gemma":[0.07097142,0.0001181588,0.9264312,0.0001245511,0.00004778355,0.000129049,0.0003213007,0.0001145276,0.001742063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003482686,"threshold_uncertainty_score":0.008604169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02213183234841923,"score_gpt":0.2712496671207468,"score_spread":0.2491178347723276,"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."}}