{"id":"W2887884712","doi":"10.11159/mvml18.104","title":"A Model Based on Clustering and Association Rules for Detection of Fraud in Banking Transactions","year":2018,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Association rule learning; Cluster analysis; Computer science; Association (psychology); Data modeling; Data mining; Artificial intelligence; Database; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000523379,0.0000802466,0.0001379745,0.0003236244,0.0001145381,0.0001255248,0.0002669473,0.00003219816,2.566736e-8],"category_scores_gemma":[0.00004724368,0.00006399241,0.00001619476,0.0005747723,0.00005929518,0.0002380007,0.00003674657,0.00009200244,2.088008e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005897032,"about_ca_system_score_gemma":0.00001723088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007603015,"about_ca_topic_score_gemma":0.00000183979,"domain_scores_codex":[0.9992017,0.000004246009,0.0001834809,0.0002486887,0.0002091003,0.0001528002],"domain_scores_gemma":[0.9994921,0.0001106228,0.0001304591,0.0000841906,0.0001499415,0.00003268294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000179421,0.0002995566,0.005680108,0.001337622,0.00004828521,3.330888e-7,0.001274196,0.100361,0.3441574,0.291169,0.0001444149,0.2553486],"study_design_scores_gemma":[0.0001517023,0.0001303002,0.002140045,0.0002051731,0.00000241523,0.000001513023,0.000001494249,0.9733064,0.02382938,0.0001312829,0.00003401367,0.00006624895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08285524,0.00002404192,0.9164854,0.0001182157,0.0002209451,0.0002122897,0.000001985762,0.00004687979,0.00003501559],"genre_scores_gemma":[0.9862922,0.000004791698,0.01360323,0.00002350801,0.00002378564,0.00002637944,4.51711e-8,0.000003602305,0.00002241813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.903437,"threshold_uncertainty_score":0.2609535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01023935273348222,"score_gpt":0.2207518741024379,"score_spread":0.2105125213689556,"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."}}