{"id":"W4387757194","doi":"10.23977/acss.2023.070810","title":"An Approach of Improved Traversal Merging of Transaction Data for Faster Apriori Algorithm","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tree traversal; Apriori algorithm; Association rule learning; Computer science; Data mining; Benchmark (surveying); A priori and a posteriori; Algorithm; Preprocessor; Data pre-processing; Merge (version control); Artificial intelligence; Parallel computing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.003354186,0.001521682,0.001927147,0.004106279,0.001469243,0.0025786,0.003356205,0.001123838,0.003122956],"category_scores_gemma":[0.009566328,0.001168945,0.002039191,0.008817929,0.0008754819,0.005650918,0.002783866,0.002097815,0.001723116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008763975,"about_ca_system_score_gemma":0.003958717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004588248,"about_ca_topic_score_gemma":0.004233394,"domain_scores_codex":[0.9949691,0.0008749691,0.0006195464,0.001156469,0.001961766,0.0004181912],"domain_scores_gemma":[0.9960166,0.000931827,0.0003932897,0.0009744466,0.001522433,0.0001614502],"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":[0.001037223,0.000399909,0.007534902,0.0004572191,0.0003726733,0.0006461387,0.001010448,0.05708292,0.05230294,0.03862156,0.0128988,0.8276352],"study_design_scores_gemma":[0.0002020848,0.0006789141,0.00290348,0.00004403739,0.0002201584,0.001750397,0.0004760477,0.8917553,0.04268109,0.0280136,0.03109292,0.0001818896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01263739,0.0003754525,0.9837356,0.0001709312,0.00007209692,0.0001467655,0.0002033159,0.001823191,0.0008352975],"genre_scores_gemma":[0.08282483,0.0003489016,0.9130785,0.0001369574,0.00008568444,0.0002730093,0.001470221,0.0002434033,0.001538517],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004588248,"threshold_uncertainty_score":0.01773888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984186272003694,"score_gpt":0.3096676508301591,"score_spread":0.2698257881101221,"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."}}