{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001852644,0.0003257583,0.0003038232,0.00004938049,0.0003889638,0.0003808255,0.001436879,0.0001447195,0.00001253949],"category_scores_gemma":[0.000005816997,0.0002867402,0.0001774024,0.001042112,0.00005141338,0.0005123429,0.000283374,0.0005442005,0.00001829172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002603656,"about_ca_system_score_gemma":0.00001159305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004128397,"about_ca_topic_score_gemma":0.000003392291,"domain_scores_codex":[0.9975502,0.00009319266,0.0003949906,0.0007519245,0.0002809579,0.0009286788],"domain_scores_gemma":[0.9984642,0.00008475446,0.0001442312,0.0009806862,0.00005280225,0.0002733061],"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":[0.000008265647,0.00005336101,0.00009652486,0.000001510576,0.000005056592,0.00001241157,0.00002296894,0.7908273,0.00001887239,0.007492265,0.01067127,0.1907902],"study_design_scores_gemma":[0.0002348554,0.0001253421,0.002634758,0.000007245827,0.000007562036,0.00009690862,0.000002372363,0.9905965,0.000003893868,0.00136156,0.004610788,0.0003182448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006007666,0.0007407554,0.9799489,0.004140926,0.000829189,0.0004658127,7.489745e-7,0.0006019235,0.007264077],"genre_scores_gemma":[0.9623036,0.00003945525,0.03010256,0.005491762,0.001728086,0.00003412413,0.0000129704,0.00001862015,0.000268834],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9562959,"threshold_uncertainty_score":0.9999585,"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."}}