{"id":"W2751548166","doi":"10.5539/mas.v11n9p151","title":"Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tertiary Education Trust Fund","keywords":"Computer science; Purchasing; Unsupervised learning; Database transaction; Behavioral pattern; Market segmentation; Partition (number theory); Data mining; Artificial intelligence; Knowledge management; Business; Marketing; Database; Mathematics","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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004552544,0.0001033947,0.00009552021,0.0001459301,0.00162275,0.001255958,0.0003087204,0.00005175748,0.00002421109],"category_scores_gemma":[0.00004618718,0.0001016179,0.0000230664,0.0001181663,0.0002533905,0.001290997,0.0001266165,0.0001101909,0.00006546779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003531858,"about_ca_system_score_gemma":0.00001896801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006807039,"about_ca_topic_score_gemma":0.00001682486,"domain_scores_codex":[0.9989692,0.00000207348,0.0001335936,0.0003724594,0.0002866306,0.000236061],"domain_scores_gemma":[0.9993982,0.00001604639,0.000201914,0.0002694506,0.00009604776,0.00001832937],"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":[0.00005594755,0.00008486277,0.03569165,0.00008414745,0.000004017065,8.473207e-7,0.00067731,0.00004706406,0.3892542,0.06562694,0.00007960319,0.5083934],"study_design_scores_gemma":[0.001295367,0.00002070053,0.3777266,0.00005832866,0.0000736427,0.000001122485,0.001151295,0.5739271,0.0017186,0.04133269,0.002139267,0.000555319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6604234,0.000007466394,0.3328006,0.0004859662,0.0001898746,0.0003705888,0.000001303918,0.0000822239,0.005638575],"genre_scores_gemma":[0.9978875,0.000002735705,0.001275189,0.0004129849,0.0002651884,0.00005928809,0.00001804618,0.00001378934,0.00006527385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5738801,"threshold_uncertainty_score":0.9997808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07425045765811449,"score_gpt":0.3257252671083438,"score_spread":0.2514748094502293,"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."}}