{"id":"W1603563923","doi":"10.1023/a:1022419032620","title":"Extracting Share Frequent Itemsets with Infrequent Subsets","year":2003,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data mining; Association rule learning; Computer science; Measure (data warehouse); Property (philosophy); Database transaction; Heuristic; Set (abstract data type); Closure (psychology); Artificial intelligence; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0004665361,0.0002126196,0.0001939991,0.00008807794,0.0003267881,0.0008588638,0.001137236,0.0000542385,0.00001030664],"category_scores_gemma":[0.0001899791,0.00017483,0.00001786299,0.0004215479,0.00007183883,0.002806284,0.0005901269,0.0001418536,0.00003576881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002694981,"about_ca_system_score_gemma":0.0002010904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004510021,"about_ca_topic_score_gemma":0.0000810554,"domain_scores_codex":[0.9983446,0.0000616669,0.0002516362,0.0008269906,0.0001715591,0.0003435476],"domain_scores_gemma":[0.9978484,0.0001975096,0.0001215607,0.001619093,0.00006933257,0.0001441128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001269997,0.0009451907,0.02859594,0.0002336326,0.0002340454,0.0001683326,0.00875432,0.00001378369,0.001032765,0.09543027,0.05561472,0.8089643],"study_design_scores_gemma":[0.004752751,0.0006417733,0.03096133,0.002341421,0.000285518,0.001929316,0.009515189,0.1052961,0.005046555,0.001219269,0.8332815,0.00472927],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4677783,0.00708356,0.4848585,0.0006732441,0.00104384,0.0007031217,0.00333736,0.0006667371,0.03385535],"genre_scores_gemma":[0.7797363,0.000152396,0.2164375,0.0001604616,0.000146455,0.00005784211,0.002087215,0.00004270047,0.001179089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.804235,"threshold_uncertainty_score":0.8282044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.051266264070474,"score_gpt":0.2920698709287232,"score_spread":0.2408036068582492,"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."}}