{"id":"W2123581730","doi":"10.1109/ictai.2007.86","title":"Exploratory Quantitative Contrast Set Mining: A Discretization Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Contrast (vision); Categorical variable; Discretization; Ranking (information retrieval); Set (abstract data type); Association rule learning; Computer science; Data mining; Measure (data warehouse); Interval (graph theory); Mathematics; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003136803,0.0006060289,0.001119882,0.003604197,0.0005345109,0.001973593,0.00169702,0.0008741195,0.001355548],"category_scores_gemma":[0.01557429,0.0004272319,0.0009984899,0.003002511,0.0008312418,0.001947463,0.001889409,0.001277459,0.0002208602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006881692,"about_ca_system_score_gemma":0.0006117536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005667128,"about_ca_topic_score_gemma":0.0008256536,"domain_scores_codex":[0.9977025,0.0007533656,0.0001988726,0.0003482875,0.0008712299,0.0001257454],"domain_scores_gemma":[0.9921325,0.005634699,0.0005593631,0.0008604457,0.0006831358,0.0001299206],"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":[0.0006469408,0.0005169105,0.02492275,0.0006712896,0.0003469019,0.0007059367,0.001322578,0.1990174,0.02916567,0.1221674,0.004075909,0.6164403],"study_design_scores_gemma":[0.00005860399,0.000167519,0.002748472,0.00008032859,0.0000597153,0.0005549253,0.0002764356,0.9041517,0.007532754,0.08049281,0.003827765,0.00004896892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02646318,0.0001919463,0.9713757,0.000146163,0.00001722962,0.000132302,0.0002420613,0.0002496606,0.001181818],"genre_scores_gemma":[0.306785,0.0001606741,0.6915385,0.0001046741,0.00003975612,0.0002715959,0.0006359741,0.00004594617,0.000417749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003604197,"threshold_uncertainty_score":0.01658916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05002360354283416,"score_gpt":0.3009474250412551,"score_spread":0.250923821498421,"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."}}