{"id":"W26972093","doi":"10.1016/j.nephro.2016.01.007","title":"VP: an Efficient Algorithm for Frequent Itemset Mining.","year":2008,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Algorithm design; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004226826,0.002977822,0.002506283,0.007109278,0.001403866,0.002869031,0.00468674,0.002782491,0.007450587],"category_scores_gemma":[0.01918821,0.001787202,0.003041846,0.007264731,0.000577745,0.003861896,0.002725932,0.002310066,0.007117596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007351555,"about_ca_system_score_gemma":0.002955586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003775204,"about_ca_topic_score_gemma":0.004565204,"domain_scores_codex":[0.9973068,0.0008265633,0.0005298181,0.000610175,0.0005605836,0.0001661654],"domain_scores_gemma":[0.9937696,0.004699823,0.000277045,0.0005645884,0.0005473984,0.0001414507],"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":[0.0009438499,0.000361421,0.004918448,0.001511472,0.0008769218,0.0004828752,0.0002904908,0.06008714,0.002786095,0.007350006,0.05679831,0.863593],"study_design_scores_gemma":[0.0004500659,0.0002554698,0.001097434,0.0001658543,0.0001759283,0.0009484054,0.0002040337,0.9350729,0.002715866,0.0352306,0.02362873,0.00005477317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005741519,0.001442345,0.9541216,0.000416172,0.0002260602,0.0008550039,0.006411191,0.02971543,0.001070694],"genre_scores_gemma":[0.02689245,0.000444347,0.9598359,0.0001412363,0.00007100059,0.001203779,0.009910403,0.0006243915,0.0008765175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007450587,"threshold_uncertainty_score":0.0249247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514132827245247,"score_gpt":0.2297649947016127,"score_spread":0.2146236664291603,"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."}}