{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006471297,0.0001080276,0.0002863109,0.000132096,0.00004806941,0.00006547206,0.0008160397,0.00004178355,2.078293e-7],"category_scores_gemma":[0.000002797952,0.00009745466,0.0000274133,0.0003764295,0.00003847414,0.00119073,0.000103635,0.00005161491,2.852473e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006225958,"about_ca_system_score_gemma":0.00002090778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004567809,"about_ca_topic_score_gemma":0.000002650924,"domain_scores_codex":[0.9987499,0.00004942477,0.0004114652,0.0004695365,0.0001439234,0.0001757879],"domain_scores_gemma":[0.9988997,0.0001484335,0.0001679453,0.0006781986,0.00005994668,0.00004576114],"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":[0.000005030654,0.00008988081,0.00005614245,0.0002612342,0.00001919469,6.204056e-7,0.0007332558,0.01083282,0.001932145,0.001249646,0.00003944781,0.9847806],"study_design_scores_gemma":[0.0003886852,0.0001269546,0.0001152973,0.0000594994,0.000005808593,0.000004621735,0.00018794,0.9974758,0.0002673881,0.0001641061,0.001097928,0.0001059511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002188404,0.0005616292,0.9962732,0.00002050862,0.0003454587,0.0003356542,0.000215202,0.00004666129,0.00001327149],"genre_scores_gemma":[0.3004023,0.0002206287,0.6989523,0.000009991531,0.0001532962,0.00007169331,0.0001668663,0.00001218681,0.00001079381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.986643,"threshold_uncertainty_score":0.3974085,"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."}}