{"id":"W1557693712","doi":"10.1007/978-3-540-30499-9_23","title":"An Efficient Two-Level SOMART Document Clustering Through Dimensionality Reduction","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Dimensionality reduction; Artificial intelligence; Adaptive resonance theory; Pattern recognition (psychology); Clustering high-dimensional data; Artificial neural network; Data mining; Brown clustering; Hierarchical clustering; Reduction (mathematics); Entropy (arrow of time); Dimension (graph theory); Document clustering; Correlation clustering; Curse of dimensionality; Canopy clustering algorithm; Mathematics","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":[0.0005850286,0.0004825362,0.0003873282,0.0002343282,0.0005695492,0.0005675243,0.002329875,0.0001816622,0.00002320075],"category_scores_gemma":[0.000009026659,0.0004347971,0.0001231942,0.0005360547,0.0005034517,0.000678729,0.001096518,0.000635272,0.0000357091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005327562,"about_ca_system_score_gemma":0.0003947687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009958122,"about_ca_topic_score_gemma":0.00005390055,"domain_scores_codex":[0.9960072,0.00003662611,0.0005381832,0.001804476,0.0009872885,0.0006262118],"domain_scores_gemma":[0.9975231,0.0001141078,0.0002728565,0.001708596,0.000190233,0.0001911316],"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":[0.00000303882,0.0000613561,0.00000164383,0.00001109943,0.000004438801,0.00001540907,0.0002959868,0.7607423,0.0004365157,0.1053902,0.000006969299,0.133031],"study_design_scores_gemma":[0.0002832997,0.0001393539,0.00007268507,0.000252939,0.000006823802,0.0001122531,1.731367e-7,0.6485785,0.001489779,0.3481802,0.0003256728,0.0005582777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006470918,0.000141702,0.9940697,0.001549483,0.001872846,0.0005143536,0.000004891715,0.0002039883,0.0009959306],"genre_scores_gemma":[0.4470258,0.00001881772,0.5513715,0.0007856711,0.0006440937,0.00002033406,0.000009589644,0.00002573542,0.0000984234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4463787,"threshold_uncertainty_score":0.9998104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858114946698372,"score_gpt":0.2865607238458167,"score_spread":0.257979574378833,"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."}}