{"id":"W4294311511","doi":"10.1109/tetci.2022.3201620","title":"Viewpoint-Based Kernel Fuzzy Clustering With Weight Information Granules","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Anhui Provincial Key Research and Development Plan; Natural Science Foundation of Anhui Province; Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Cluster analysis; Fuzzy clustering; Mathematics; Artificial intelligence; Data mining; Pattern recognition (psychology); FLAME clustering; Correlation clustering; CURE data clustering algorithm; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003353922,0.0001883066,0.0001605984,0.0005909022,0.0005051771,0.0001112932,0.0008206969,0.0000344977,0.0000599798],"category_scores_gemma":[0.000008386002,0.0001998527,0.00006285319,0.001116357,0.00007084729,0.0009696419,0.00002616928,0.0006197058,0.00002714664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003720142,"about_ca_system_score_gemma":0.0001723759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005970981,"about_ca_topic_score_gemma":0.00003105044,"domain_scores_codex":[0.9978727,0.0001269061,0.0004365275,0.0003625549,0.0008663341,0.0003350015],"domain_scores_gemma":[0.999045,0.0002691995,0.0001076139,0.0003501435,0.0001505186,0.00007757283],"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":[0.00003208557,0.00008202843,0.000005935116,0.00002231239,0.000008546014,0.00001263431,0.0004520409,0.8441563,0.000003701302,0.005422325,0.000005860921,0.1497962],"study_design_scores_gemma":[0.0002514929,0.0001880647,0.00007269561,0.0000428814,0.000002557208,0.00002832672,0.0001248767,0.9858555,0.0008717747,0.0112588,0.001072344,0.0002307409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000630437,0.00002193244,0.994786,0.003292782,0.0005831601,0.0002535157,0.0000120204,0.0001641631,0.0002559883],"genre_scores_gemma":[0.6831065,0.00001350929,0.3154348,0.001089907,0.00002724071,0.0002153843,0.000008785523,0.00001566192,0.00008828114],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.682476,"threshold_uncertainty_score":0.8149755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503364515159577,"score_gpt":0.2962129579615027,"score_spread":0.2711793128099069,"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."}}