{"id":"W4232700090","doi":"10.4018/9781599041629.ch002","title":"Bi-Directional Constraint Pushing in Frequent Pattern Mining","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Association rule learning; Constraint (computer-aided design); Key (lock); Data mining; Process (computing); Affinity analysis; Contrast (vision); Resource (disambiguation); Eclipse; Artificial intelligence; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001948476,0.0003303019,0.0003146697,0.0001154919,0.0001035695,0.0002006602,0.0009940959,0.0002397626,0.00004231738],"category_scores_gemma":[0.0000104426,0.0003500105,0.0001190577,0.00003563639,0.0001072402,0.0001301251,0.000413635,0.0003110314,0.0001644839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000226431,"about_ca_system_score_gemma":0.0002329958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005854586,"about_ca_topic_score_gemma":0.0003462297,"domain_scores_codex":[0.998155,0.00001302557,0.0004427495,0.0007262924,0.0003211062,0.0003418057],"domain_scores_gemma":[0.9988293,0.00004498476,0.0002211594,0.0006903874,0.00006880242,0.0001453377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[7.235205e-7,0.00001026004,0.00004343317,0.000006295947,0.00002253134,0.00005533591,0.0001249824,0.000001396717,0.00000674685,0.7608064,0.0007639675,0.2381579],"study_design_scores_gemma":[0.0008268264,0.0001533016,0.001053308,0.001211873,0.00005404212,0.0005535683,0.00004491091,0.003492707,0.0001157981,0.8543609,0.1362729,0.001859871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.00008238642,0.0001112773,0.1083077,0.0001112081,0.0005213615,0.0001985087,0.0002430746,0.0001991428,0.8902254],"genre_scores_gemma":[0.4530515,0.00004883734,0.430521,0.005528702,0.00225052,0.000529866,0.0001798298,0.0002809393,0.1076088],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7826166,"threshold_uncertainty_score":0.9998952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03442239182957069,"score_gpt":0.249673420421399,"score_spread":0.2152510285918283,"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."}}