{"id":"W2080533345","doi":"10.1007/s10844-006-2618-8","title":"An efficient approach to mining indirect associations","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Association rule learning; Data mining; struct; Set (abstract data type); Efficient algorithm; Algorithm","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":[0.00110203,0.00009323106,0.0001846575,0.0003985643,0.0001383898,0.0006735576,0.0007156791,0.00004666734,0.000001086865],"category_scores_gemma":[0.00005296486,0.00007788761,0.00006367611,0.0005943208,0.000007921882,0.001435266,0.00004715171,0.00009997559,0.00007118774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492942,"about_ca_system_score_gemma":0.00008467647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005085617,"about_ca_topic_score_gemma":6.185173e-7,"domain_scores_codex":[0.9982288,0.00004973596,0.000966255,0.00008186974,0.0005129919,0.0001603777],"domain_scores_gemma":[0.9982316,0.00005848306,0.0007770959,0.0002830115,0.0005303996,0.0001193787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006072369,0.0005030095,0.0007003717,0.00003709667,0.00006052285,0.000001420328,0.01124952,0.8016666,0.0001322417,0.1029642,0.02287667,0.05980236],"study_design_scores_gemma":[0.0001785412,0.000146069,0.002871629,0.0000627332,0.00001186002,0.0001280883,0.00220984,0.9259662,0.0007060907,0.00004288682,0.06746917,0.0002068846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01827225,0.00003772574,0.9740112,0.00009320778,0.0005085863,0.0001767205,0.0000228683,0.00004477329,0.006832646],"genre_scores_gemma":[0.8586429,0.000002823909,0.1408528,0.0001243722,0.0002478667,0.00001941586,0.00004368814,0.000004908475,0.00006121481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8403707,"threshold_uncertainty_score":0.6495131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02252393769148645,"score_gpt":0.2616728185588945,"score_spread":0.2391488808674081,"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."}}