{"id":"W1712321737","doi":"10.1007/978-3-540-39592-8_23","title":"Clustering Supermarket Customers Using Rough Set Based Kohonen Networks","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Self-organizing map; Cluster analysis; Computer science; Rough set; Set (abstract data type); Data mining; Artificial intelligence; Pattern recognition (psychology)","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":[0.000744996,0.0006589903,0.001095011,0.001913726,0.0006855071,0.001387289,0.001529994,0.0009897122,0.001691217],"category_scores_gemma":[0.001745629,0.000542062,0.001270641,0.002436487,0.0003384754,0.001682279,0.0005661273,0.0006443503,0.0005329143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006638593,"about_ca_system_score_gemma":0.000579385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00964358,"about_ca_topic_score_gemma":0.01008671,"domain_scores_codex":[0.9994561,0.00008761131,0.00004752582,0.0001108136,0.0002005204,0.00009735421],"domain_scores_gemma":[0.9994098,0.0002384341,0.00005222834,0.00005619423,0.0002204112,0.00002299793],"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":[0.0006580228,0.0003529004,0.008904316,0.0001734905,0.00029732,0.0002924503,0.0004707374,0.5948771,0.007936812,0.004307197,0.003577026,0.3781526],"study_design_scores_gemma":[0.000006240123,0.00002587745,0.0009780433,0.000005319208,0.00002056506,0.00002223895,0.00008621087,0.9956331,0.000966298,0.002030185,0.000210008,0.00001588878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2854173,0.0006433108,0.7081237,0.0002635674,0.0001056108,0.0002132733,0.0003285552,0.0007104407,0.004194271],"genre_scores_gemma":[0.8320073,0.0003025015,0.1635733,0.00005277032,0.00004882221,0.0001181177,0.0005248547,0.00004276374,0.003329527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00964358,"threshold_uncertainty_score":0.01917493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02801572617262131,"score_gpt":0.2479500524554208,"score_spread":0.2199343262827994,"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."}}