{"id":"W2140168239","doi":"10.1109/cse.2009.51","title":"Scalable APRIORI-Based Frequent Pattern Discovery","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Scalability; Benchmark (surveying); A priori and a posteriori; Data mining; sort; Task (project management); Field (mathematics); Machine learning; Implementation; Contrast (vision); Artificial intelligence; Pattern recognition (psychology); Information retrieval; 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":[0.003154281,0.001086346,0.002110328,0.002451196,0.0008245702,0.002182695,0.00334126,0.0008247438,0.003335984],"category_scores_gemma":[0.01003806,0.0007861122,0.001039089,0.005174477,0.0005074929,0.00439824,0.001906898,0.001317402,0.002843513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005175008,"about_ca_system_score_gemma":0.002034922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003021589,"about_ca_topic_score_gemma":0.004310034,"domain_scores_codex":[0.997559,0.0004349962,0.0002350125,0.0005378688,0.001078792,0.000154373],"domain_scores_gemma":[0.992125,0.003139891,0.000503045,0.002471712,0.001488144,0.0002722385],"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":[0.001378192,0.000591659,0.007516371,0.0006376455,0.0003597894,0.0006601195,0.0002047845,0.07970773,0.01880398,0.0102391,0.03390411,0.8459964],"study_design_scores_gemma":[0.0002799174,0.0002655435,0.00188322,0.0000334602,0.00009893096,0.0007898255,0.0001476098,0.9502656,0.01132384,0.02488004,0.00998096,0.00005110043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0656898,0.002832782,0.8967103,0.001470694,0.0003339234,0.0004525926,0.003997941,0.02245088,0.006061094],"genre_scores_gemma":[0.1949789,0.0009811081,0.7926264,0.0002679176,0.0002222657,0.0003032153,0.007700912,0.0002580931,0.002661181],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00334126,"threshold_uncertainty_score":0.01668161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441392165324489,"score_gpt":0.2457606988023582,"score_spread":0.2313467771491133,"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."}}