{"id":"W2160422165","doi":"10.1016/j.neunet.2009.08.007","title":"Clustering: A neural network approach","year":2009,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":325,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Fuzzy clustering; Correlation clustering; Learning vector quantization; Artificial intelligence; Pattern recognition (psychology); CURE data clustering algorithm; Computer science; Consensus clustering; Canopy clustering algorithm; Data stream clustering; Single-linkage clustering; Conceptual clustering; Neural gas; Hierarchical clustering; Data mining; Competitive learning; Vector quantization; Unsupervised learning; Artificial neural network; Time delay neural network","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":[0.00146465,0.0009788325,0.001842672,0.003580275,0.001343034,0.002200863,0.003227154,0.002608679,0.00491282],"category_scores_gemma":[0.00457376,0.0008055227,0.001333096,0.004304067,0.0009399533,0.002636285,0.001629732,0.001145576,0.001558711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326512,"about_ca_system_score_gemma":0.0009257067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006234679,"about_ca_topic_score_gemma":0.00689421,"domain_scores_codex":[0.9985569,0.0004020259,0.00008682696,0.0004293562,0.0004366053,0.00008829856],"domain_scores_gemma":[0.9986047,0.0005896582,0.0001115086,0.0002007453,0.0004465184,0.00004678553],"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":[0.000178963,0.0001367961,0.001693093,0.000478174,0.0004587098,0.0001267163,0.000199271,0.4440452,0.003786751,0.1044632,0.01350217,0.4309311],"study_design_scores_gemma":[0.00001355661,0.0000259209,0.0006316851,0.00003322379,0.00005627896,0.00009664888,0.00005282943,0.9323176,0.001447539,0.06086355,0.004433065,0.0000279949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003320128,0.0008815172,0.9923589,0.0002635455,0.0001001296,0.00006348201,0.0001352645,0.0004286092,0.002448529],"genre_scores_gemma":[0.2022249,0.001885564,0.7773969,0.000286579,0.0004462962,0.0003051238,0.0009695895,0.0004603853,0.01602476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006234679,"threshold_uncertainty_score":0.01643503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01879314230365507,"score_gpt":0.2398786784885903,"score_spread":0.2210855361849353,"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."}}