{"id":"W4397037919","doi":"10.21275/sr23309130518","title":"Frequent Pattern Matching in Data Mining using Modified Apriori Algorithm with Portioned Data Set Approaches","year":2023,"lang":"en","type":"article","venue":"International Journal of Science and Research (IJSR)","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Apriori algorithm; Data mining; Data set; Computer science; Matching (statistics); A priori and a posteriori; Set (abstract data type); Algorithm; Association rule learning; Artificial intelligence; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01243462,0.00009744292,0.0001793792,0.001048386,0.000276057,0.0008461418,0.006555336,0.00003613169,0.0000014524],"category_scores_gemma":[0.0003335132,0.00006727525,0.00001582496,0.001351002,0.0004967927,0.003372906,0.002827381,0.0003541976,0.000003725218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000130702,"about_ca_system_score_gemma":0.0009072958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004825422,"about_ca_topic_score_gemma":0.00003733167,"domain_scores_codex":[0.9951991,0.0001491224,0.0004501629,0.0005260766,0.003253075,0.000422394],"domain_scores_gemma":[0.9979915,0.0002136969,0.000226814,0.0006828702,0.0007202329,0.000164898],"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":[0.0001153698,0.0002726331,0.02086217,0.00005320783,0.0002205993,0.003693261,0.01007625,0.008193496,0.009074579,0.005157982,0.001547074,0.9407334],"study_design_scores_gemma":[0.0007227455,0.00006748026,0.009390574,0.0002484358,0.000003273539,0.0005090046,0.00358951,0.9823425,0.00005999265,0.00280755,0.0001339246,0.0001250275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7102585,0.0003174049,0.2791186,0.008212615,0.001021191,0.0002480164,0.00006598705,0.00003269747,0.000724983],"genre_scores_gemma":[0.9849525,0.0000766775,0.01458945,0.00006383687,0.0002629418,0.000002038183,0.00001349896,0.000005310093,0.00003373783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.974149,"threshold_uncertainty_score":0.9988196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4413681804974323,"score_gpt":0.4275281555893592,"score_spread":0.01384002490807301,"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."}}