{"id":"W1525895928","doi":"10.1002/sam.11394","title":"Standardizing interestingness measures for association rules","year":2018,"lang":"en","type":"preprint","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; McMaster University; Thompson Rivers University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Measure (data warehouse); Lift (data mining); Computer science; Raw data; Association rule learning; Standardization; Association (psychology); Data mining; Value (mathematics); Machine learning; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07722302,0.001943742,0.003058753,0.01295815,0.001592586,0.008040673,0.003218813,0.002910034,0.001312434],"category_scores_gemma":[0.352929,0.0009869298,0.002384998,0.01010947,0.006530418,0.01120829,0.005365108,0.005090179,0.0004146571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002504399,"about_ca_system_score_gemma":0.002010335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004579275,"about_ca_topic_score_gemma":0.0004935826,"domain_scores_codex":[0.9270636,0.03142687,0.009621611,0.01093862,0.01975781,0.001191477],"domain_scores_gemma":[0.577021,0.3136332,0.02863873,0.05472247,0.02371471,0.002269953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001030156,0.0006186695,0.05924733,0.001568077,0.002246059,0.0004287225,0.001802373,0.1501608,0.01064286,0.3957886,0.004936362,0.3715301],"study_design_scores_gemma":[0.0001554737,0.0007280725,0.009950608,0.0004200702,0.0003363703,0.000639069,0.0004844565,0.2333934,0.01209675,0.7362395,0.005364089,0.0001920911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1204969,0.002910347,0.8687267,0.001125294,0.0002620534,0.0004325282,0.0009680514,0.0007440138,0.004334023],"genre_scores_gemma":[0.5855612,0.0008490371,0.4091456,0.0004138547,0.000455046,0.001100503,0.001713499,0.0003093128,0.0004520086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07722302,"threshold_uncertainty_score":0.4083992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1201164593375698,"score_gpt":0.3973333708886629,"score_spread":0.2772169115510931,"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."}}