{"id":"W2157169143","doi":"10.1109/icdm.2012.20","title":"Direct Discovery of High Utility Itemsets without Candidate Generation","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Data mining; Pruning; Bounding overwatch; Property (philosophy); Artificial intelligence; 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":[0.003857282,0.0009950185,0.002255677,0.003457578,0.0008924085,0.001626152,0.00290541,0.001352299,0.001476542],"category_scores_gemma":[0.01880302,0.0006861231,0.00143551,0.004280138,0.0008839745,0.002946792,0.001921192,0.00139474,0.0009645682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000532463,"about_ca_system_score_gemma":0.001331796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000892253,"about_ca_topic_score_gemma":0.001927675,"domain_scores_codex":[0.9978101,0.0006567533,0.0001835442,0.0004041546,0.0007587887,0.0001866812],"domain_scores_gemma":[0.9826348,0.01266474,0.001053575,0.002069143,0.001289612,0.0002881175],"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":[0.00084117,0.000845897,0.02396401,0.0006575735,0.0002436475,0.001005921,0.0005525041,0.1174271,0.01265085,0.03124073,0.004893419,0.8056771],"study_design_scores_gemma":[0.0001177705,0.0003395166,0.002494989,0.00008563579,0.0001033235,0.0008598744,0.0001817209,0.9443722,0.009081717,0.03915498,0.003172971,0.00003522848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07352252,0.0005431063,0.9225376,0.0002578428,0.00003485299,0.0004583084,0.000290837,0.001038494,0.001316487],"genre_scores_gemma":[0.3580469,0.0003348658,0.6377784,0.0001765934,0.00006663569,0.0004146486,0.001301735,0.00009319644,0.00178703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003857282,"threshold_uncertainty_score":0.02039951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02712208151631306,"score_gpt":0.2701453798814657,"score_spread":0.2430232983651526,"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."}}