{"id":"W3122289361","doi":"10.18280/ria.340604","title":"Big Data Clustering Using Improvised Fuzzy C-Means Clustering","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"MNIST database; Computer science; Cluster analysis; Artificial intelligence; Fuzzy clustering; Data mining; Convolutional neural network; Big data; Rand index; Fuzzy logic; Encoder; Pattern recognition (psychology); Deep learning; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001756335,0.0009524018,0.001090249,0.002793775,0.001444386,0.001768614,0.002672495,0.001772286,0.001165296],"category_scores_gemma":[0.005080232,0.000488697,0.001556781,0.002300722,0.000883854,0.001701536,0.001071292,0.001415746,0.0004680391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002431031,"about_ca_system_score_gemma":0.00288652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02771385,"about_ca_topic_score_gemma":0.01963679,"domain_scores_codex":[0.9982306,0.0002260696,0.0001477049,0.0004671218,0.0007898404,0.0001387755],"domain_scores_gemma":[0.9978349,0.0004962072,0.0002296468,0.0002645639,0.001098861,0.00007576466],"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":[0.0003018017,0.0001463228,0.003977228,0.0002758179,0.0002789577,0.0001801734,0.000383028,0.5990008,0.00739567,0.01530576,0.004102789,0.3686517],"study_design_scores_gemma":[0.000007289652,0.00002624762,0.000486894,0.00001619862,0.0000121714,0.00004959034,0.0000401015,0.9886063,0.004153416,0.005245693,0.001331394,0.00002465606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01790597,0.0003272451,0.9786844,0.0002082109,0.00006268299,0.0001453548,0.0001683844,0.001032727,0.001465098],"genre_scores_gemma":[0.2721257,0.0003174972,0.7243342,0.00012805,0.0000480305,0.0002118995,0.0005865307,0.0001033073,0.002144866],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02771385,"threshold_uncertainty_score":0.05510503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2135487891762695,"score_gpt":0.3465995114768949,"score_spread":0.1330507223006255,"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."}}