{"id":"W2071386504","doi":"10.1145/1982185.1982399","title":"Equivalence class transformation based mining of frequent itemsets from uncertain data","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Probabilistic logic; Data mining; Transformation (genetics); Equivalence (formal languages); Computer science; Equivalence class (music); Class (philosophy); Uncertain data; Data transformation; Mathematics; Artificial intelligence; Data warehouse; Discrete mathematics","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":[0.000246292,0.00008107397,0.0001018781,0.00005036789,0.00005558142,0.00004261445,0.001862801,0.00003426256,0.000118648],"category_scores_gemma":[0.00001888571,0.00007135912,0.00001818159,0.0002687505,0.00003756515,0.0009334903,0.0001887568,0.00004669878,0.00002403762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000118629,"about_ca_system_score_gemma":0.00006954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103524,"about_ca_topic_score_gemma":0.00003625185,"domain_scores_codex":[0.9990706,0.00002495301,0.0002634927,0.000313921,0.0001896985,0.0001373189],"domain_scores_gemma":[0.9984081,0.00007457987,0.00009398885,0.001309187,0.00005686437,0.00005721782],"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":[0.00001043461,0.0005563309,0.002289874,0.00005841352,0.00006776814,0.000008482727,0.009955445,0.0001295386,0.007856865,0.1409468,0.01245502,0.825665],"study_design_scores_gemma":[0.0002418889,0.00003785975,0.00352005,0.00003779759,0.00001055349,0.000001205493,0.0001879193,0.9786508,0.01267531,0.0009064948,0.003577448,0.0001526598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01290823,0.00002029453,0.9802346,0.0003582075,0.0000752765,0.00009580721,0.0003137897,0.00009249587,0.005901275],"genre_scores_gemma":[0.4542046,0.000003950682,0.5452344,0.0001429026,0.00001210341,0.000007174681,0.0003676785,0.000003612934,0.00002351211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9785213,"threshold_uncertainty_score":0.3461578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1754554984814219,"score_gpt":0.3027612175957297,"score_spread":0.1273057191143079,"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."}}