{"id":"W1998319238","doi":"10.4018/jdwm.2005100103","title":"Preference-Based Frequent Pattern Mining","year":2005,"lang":"en","type":"article","venue":"International Journal of Data Warehousing and Mining","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Preference; Data mining; Sequential Pattern Mining; Constraint (computer-aided design); K-optimal pattern discovery; Machine learning; Artificial intelligence; Data stream mining; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0005136557,0.0001098909,0.0001413618,0.0001905798,0.00009517387,0.000409804,0.002048084,0.00003453549,0.00001195052],"category_scores_gemma":[0.00008929371,0.00009812839,0.00003116854,0.00009837413,0.00003747204,0.001585694,0.0005168078,0.0001314085,0.00000447098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004607409,"about_ca_system_score_gemma":0.0001445982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000281982,"about_ca_topic_score_gemma":0.00001491303,"domain_scores_codex":[0.9987043,0.00002977248,0.000420488,0.0002646071,0.0004284539,0.0001524046],"domain_scores_gemma":[0.9987338,0.000149803,0.0003562828,0.0004279755,0.0002335035,0.000098671],"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":[0.000003723967,0.00005162604,0.0009972539,0.000002712233,0.00004164054,0.00002455382,0.0005846141,0.0001982404,0.0001454486,0.00009331469,0.002195613,0.9956613],"study_design_scores_gemma":[0.001542466,0.0001495349,0.00261346,0.0006835218,0.00004380678,0.0006620946,0.0004203791,0.8293599,0.0007748639,0.0001177697,0.1632533,0.0003788002],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1194875,0.0003784098,0.8750373,0.004191361,0.0004803753,0.00003377598,0.00009761994,0.00003907957,0.0002545745],"genre_scores_gemma":[0.5607445,0.00004122365,0.4382665,0.0004661617,0.0004118987,0.000001011415,0.00004122799,0.000007006657,0.00002041637],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9952825,"threshold_uncertainty_score":0.400156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1167447425351349,"score_gpt":0.3223307267655304,"score_spread":0.2055859842303955,"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."}}