{"id":"W4408352835","doi":"10.1109/icassp49660.2025.10890745","title":"Weighted Density for The Win: Accurate Subspace Density Clustering","year":2025,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Science Foundation of Fujian Province; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Subspace topology; Computer science; Artificial intelligence","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":[0.0002456467,0.0001075612,0.0001092982,0.0000945364,0.0004038374,0.0003031593,0.000150725,0.00003795034,0.00009151625],"category_scores_gemma":[0.0000477646,0.00007409117,0.00007024215,0.0003333422,0.00002612197,0.000450293,0.0001465408,0.00006279566,0.00008142044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002825077,"about_ca_system_score_gemma":0.00001199492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005750266,"about_ca_topic_score_gemma":0.002363926,"domain_scores_codex":[0.9993982,0.000004934531,0.0001401712,0.0001776991,0.00009864377,0.0001803603],"domain_scores_gemma":[0.9994962,0.0001037665,0.00006984051,0.0001821101,0.0001430714,0.000005072495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001381936,0.0003438091,0.1710817,0.001490872,0.0007051703,0.00001874302,0.0005797054,0.000706088,0.01785593,0.2631165,0.3951646,0.1475549],"study_design_scores_gemma":[0.003252611,0.00001090402,0.262138,0.00009396804,0.0005066893,0.000002622281,0.002714017,0.5117512,0.006346464,0.009274731,0.2032389,0.0006698096],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6160421,0.00007910685,0.3351542,0.01492701,0.001643361,0.001037937,0.000001230791,0.0003176358,0.03079733],"genre_scores_gemma":[0.9874529,0.000007349558,0.0003321056,0.006282803,0.000339476,0.00002129167,0.00001516282,0.000008765349,0.005540178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5110452,"threshold_uncertainty_score":0.3106032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02237723229451121,"score_gpt":0.2584868484276723,"score_spread":0.2361096161331611,"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."}}