{"id":"W2522166539","doi":"10.5539/mas.v10n10p283","title":"Customer Segmentation of Bank Based on Discovering of Their Transactional Relation by Using Data Mining Algorithms","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Loan; Database transaction; Transaction data; Market segmentation; Relation (database); Customer relationship management; Transactional leadership; Customer intelligence; Segmentation; Credential; Customer retention; Data mining; Business; Service (business); Marketing; Finance; Database; Computer security; Artificial intelligence; Service quality","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005271038,0.0001059409,0.0001190213,0.0002656952,0.0001484468,0.0000555561,0.0003100602,0.00002777862,0.00005515237],"category_scores_gemma":[0.00001430004,0.00007849286,0.0000234392,0.0004906847,0.0001786351,0.001511365,0.00005633468,0.00003619024,0.000006924506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006610694,"about_ca_system_score_gemma":0.00004649882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005504025,"about_ca_topic_score_gemma":0.000005332236,"domain_scores_codex":[0.9986921,0.000004759651,0.0002572298,0.0003388819,0.0005462523,0.0001608026],"domain_scores_gemma":[0.9993353,0.00005105662,0.0002631304,0.0002737854,0.00006566711,0.0000110369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004521808,0.00004960826,0.0009759528,0.00002745128,0.000003959748,7.840031e-8,0.0001226307,0.003054927,0.928919,0.000189334,0.00002728016,0.06658459],"study_design_scores_gemma":[0.0007058926,0.000006141633,0.002360038,0.00006590019,0.00002081879,1.970741e-7,0.0002469724,0.9229187,0.07329818,0.0001717704,0.00007110217,0.0001342924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4118377,0.000004712446,0.5859213,0.00006686069,0.0001007836,0.0001496477,0.00003187336,0.00001958285,0.001867461],"genre_scores_gemma":[0.9969426,0.000001316336,0.002811569,0.0001059909,0.00005581857,0.000005036535,0.00004938823,0.00001107391,0.00001724795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9198638,"threshold_uncertainty_score":0.3200846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04734557453932994,"score_gpt":0.263821172656972,"score_spread":0.2164755981176421,"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."}}