{"id":"W4416703099","doi":"10.1016/j.datak.2025.102536","title":"Enhancing clustering stability, compactness, and separation in multimodal data environments","year":2025,"lang":"en","type":"article","venue":"Data & Knowledge Engineering","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministério da Ciência, Tecnologia, Inovações e Comunicações","keywords":"Cluster analysis; Benchmark (surveying); Stability (learning theory); Rand index; Hierarchical clustering; Benchmarking; Consensus clustering; Embedding; Focus (optics); Core (optical fiber)","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":[0.003780584,0.001130859,0.00132678,0.002604727,0.001318053,0.002517643,0.001401821,0.001366728,0.0009416192],"category_scores_gemma":[0.0153403,0.0006539511,0.0007854956,0.002376004,0.001067937,0.003566838,0.003726916,0.001101693,0.0004215062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001262272,"about_ca_system_score_gemma":0.001227678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005427242,"about_ca_topic_score_gemma":0.007086963,"domain_scores_codex":[0.9977767,0.0007557273,0.0001495784,0.0004859733,0.0005691308,0.000262857],"domain_scores_gemma":[0.9913042,0.005426342,0.0006867053,0.0007415349,0.001481667,0.0003596818],"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":[0.001974484,0.0007207565,0.02238409,0.0002718951,0.0003938234,0.0002581385,0.00199684,0.4792128,0.03693017,0.01038103,0.002917194,0.4425588],"study_design_scores_gemma":[0.00001748742,0.0001090199,0.003296635,0.00001126196,0.00004422143,0.0000695393,0.0003479892,0.9817245,0.008051221,0.005874475,0.000432011,0.00002159921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.30202,0.0004089859,0.6946422,0.0003330341,0.00003014343,0.00006477889,0.0001400873,0.0009149233,0.001445743],"genre_scores_gemma":[0.8682044,0.0001547877,0.1294361,0.00007271503,0.00005533324,0.00006062591,0.0004950973,0.0002240864,0.00129688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005427242,"threshold_uncertainty_score":0.0199939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04420924631857913,"score_gpt":0.2996458049990858,"score_spread":0.2554365586805067,"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."}}