{"id":"W2482088207","doi":"10.1007/978-3-319-40581-0_31","title":"Computing Theoretically-Sound Upper Bounds to Expected Support for Frequent Pattern Mining Problems over Uncertain Big Data","year":2016,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Data mining; Uncertain data; Probabilistic logic; Database transaction; Big data; Upper and lower bounds; Tree (set theory); Transaction data; Artificial intelligence; Mathematics; Database","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":[0.0135149,0.003044252,0.004211333,0.002959857,0.001423879,0.008987272,0.005966061,0.003499718,0.007909536],"category_scores_gemma":[0.1346087,0.002152871,0.003029149,0.004095298,0.004261991,0.01387366,0.006783757,0.01034717,0.001796617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003798825,"about_ca_system_score_gemma":0.003501107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001119538,"about_ca_topic_score_gemma":0.001416724,"domain_scores_codex":[0.9881903,0.003855633,0.0007483223,0.001829568,0.004222769,0.001153493],"domain_scores_gemma":[0.8402873,0.1430557,0.003063735,0.007306089,0.004323679,0.001963598],"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":[0.00148784,0.0004099398,0.003445481,0.001260751,0.0003842002,0.0002819731,0.0005195613,0.5004977,0.003193391,0.2959171,0.01766638,0.1749358],"study_design_scores_gemma":[0.00004405131,0.00008089374,0.000253039,0.0001119143,0.00003316413,0.00009211803,0.00006176137,0.6262113,0.0009033859,0.3710134,0.001170125,0.0000247556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02502789,0.00453871,0.9590544,0.003296523,0.0004764233,0.0001177108,0.0008442209,0.0009396703,0.005704578],"genre_scores_gemma":[0.49812,0.005624486,0.4812662,0.001665172,0.002958262,0.0008252228,0.00332738,0.0009097818,0.005303452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0135149,"threshold_uncertainty_score":0.07147449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09379594328863143,"score_gpt":0.3328022195833624,"score_spread":0.2390062762947309,"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."}}