{"id":"W2572924396","doi":"","title":"Intelligent Decision Making for Customer Dynamics Management Based on Rule Mining and Contrast Set Mining - A Segmentation Analysis Perspective.","year":2016,"lang":"en","type":"article","venue":"Information Reuse and Integration","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Perspective (graphical); Computer science; Contrast (vision); Data mining; Set (abstract data type); Artificial intelligence; Segmentation; Decision rule","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000368351,0.000173384,0.0001664834,0.001256647,0.000230102,0.0004630905,0.00007461366,0.00005374493,0.0000632341],"category_scores_gemma":[0.0001698102,0.0001285089,0.00006328058,0.0005094283,0.00003070832,0.002605844,0.00004689785,0.00003561144,0.00002430116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000197596,"about_ca_system_score_gemma":0.000009966314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004094624,"about_ca_topic_score_gemma":0.0001714802,"domain_scores_codex":[0.9989698,0.00001103223,0.0004129228,0.0001993848,0.0002483808,0.000158494],"domain_scores_gemma":[0.9991022,0.0001502387,0.0003068509,0.0001637202,0.0002605142,0.00001648782],"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":[0.001027685,0.00007874323,0.01315984,0.0001620772,0.0002602629,0.00000137902,0.002419468,0.001160991,0.0003439348,0.04167273,0.002139468,0.9375734],"study_design_scores_gemma":[0.002798242,0.0001230552,0.02076743,0.0004644612,0.0006376727,0.000001635893,0.02571153,0.9443949,0.000336352,0.001593452,0.002726117,0.0004451718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3977875,0.00001124951,0.5963316,0.0007614669,0.0001552391,0.0005984554,0.00002569427,0.00006954937,0.004259287],"genre_scores_gemma":[0.9902115,0.00003417447,0.007884348,0.001353989,0.00007438926,0.00007844016,0.0002911817,0.00001065744,0.00006126251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9432339,"threshold_uncertainty_score":0.5240442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587959224581643,"score_gpt":0.272794751458863,"score_spread":0.2569151592130466,"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."}}