{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00149093,0.0006516811,0.0009816563,0.002089775,0.0005310076,0.001053224,0.00165671,0.001039457,0.001526801],"category_scores_gemma":[0.002755108,0.0003734955,0.001344947,0.001876589,0.0006139863,0.0008780215,0.0008593489,0.001035832,0.0006619289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062023,"about_ca_system_score_gemma":0.001643693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110979,"about_ca_topic_score_gemma":0.01106237,"domain_scores_codex":[0.9988598,0.0003695576,0.00008441311,0.0002946116,0.000266663,0.0001250275],"domain_scores_gemma":[0.9989214,0.0004960173,0.0001214187,0.00008961499,0.0003270464,0.00004449398],"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":[0.0001745465,0.0003620606,0.007765681,0.0002378928,0.0003323316,0.0002309282,0.0003017655,0.488759,0.004770142,0.02692336,0.004651178,0.4654911],"study_design_scores_gemma":[0.000003378995,0.00002248159,0.0006088056,0.000006897081,0.000009367487,0.00002170883,0.0000208565,0.992811,0.0003288511,0.005585291,0.0005737359,0.000007621714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009281896,0.000226194,0.9886599,0.0001467434,0.00002434233,0.00007147378,0.0001866938,0.0005934946,0.0008094003],"genre_scores_gemma":[0.4305507,0.0004680991,0.5612261,0.0001848911,0.0001460591,0.0006951968,0.001541747,0.0001330339,0.005054106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0110979,"threshold_uncertainty_score":0.02206659,"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."}}