{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.01929779,0.0002778236,0.0005499746,0.0004039469,0.002224333,0.007570941,0.01576496,0.0001001082,0.00001555217],"category_scores_gemma":[0.009649204,0.0001855086,0.00006985463,0.001186096,0.0006971346,0.002425782,0.02033588,0.0006054899,0.000005263958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001459693,"about_ca_system_score_gemma":0.0009227794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001555048,"about_ca_topic_score_gemma":0.000139495,"domain_scores_codex":[0.9952362,0.0001707919,0.0007774106,0.001741008,0.001446969,0.0006276463],"domain_scores_gemma":[0.9910349,0.001724081,0.001016208,0.005064225,0.0008421538,0.0003184458],"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":[0.00002300812,0.0001173225,0.005174021,0.0000570743,0.001731102,0.00001213862,0.001179485,0.00008570164,0.00004982238,0.01584528,0.1077135,0.8680116],"study_design_scores_gemma":[0.0001512507,0.00004866697,0.008948904,0.0001008133,0.001344807,0.00002847561,0.0002878381,0.9661919,0.000008738364,0.01100594,0.01155731,0.0003253758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001910304,0.0001917156,0.9654648,0.002411145,0.0005009854,0.0001593842,0.0292683,0.00003958169,0.00005383015],"genre_scores_gemma":[0.02022904,0.0002494069,0.9743071,0.0001731096,0.0006208663,0.00001383405,0.004369766,0.00001229693,0.00002462595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9661062,"threshold_uncertainty_score":0.9990746,"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."}}