{"id":"W3048555279","doi":"10.1002/9781118445112.stat07846","title":"Dimension Reduction in Clustering","year":2016,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Dimensionality reduction; Cluster analysis; Dimension (graph theory); Curse of dimensionality; Computer science; Variable (mathematics); Transformation (genetics); Reduction (mathematics); Clustering high-dimensional data; Data mining; Mathematics; Artificial intelligence; Combinatorics","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":[0.004121811,0.0009800419,0.001771256,0.003315811,0.001030649,0.002782574,0.001500873,0.001117606,0.003071554],"category_scores_gemma":[0.01377912,0.0006164505,0.00149679,0.004191067,0.002213947,0.002105468,0.002940987,0.002333861,0.001754196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001541226,"about_ca_system_score_gemma":0.001414248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551857,"about_ca_topic_score_gemma":0.001297275,"domain_scores_codex":[0.9938337,0.00301052,0.0002760498,0.0008952092,0.001802973,0.0001815724],"domain_scores_gemma":[0.9937171,0.003026655,0.0003928714,0.001445052,0.001284936,0.0001334448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000108887,0.00005459352,0.00148636,0.001552521,0.000463925,0.0001018545,0.000593046,0.1135745,0.002993631,0.4842811,0.03553495,0.3592546],"study_design_scores_gemma":[0.00002300109,0.0000430902,0.001042581,0.000265955,0.00006793874,0.0001419345,0.0001275439,0.2768794,0.002164945,0.6789718,0.04020966,0.00006208494],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005882985,0.01255332,0.970659,0.001928395,0.0004857564,0.0001361022,0.0004343823,0.0005023952,0.007417595],"genre_scores_gemma":[0.2091119,0.01394478,0.7643163,0.00102269,0.001270947,0.0007205071,0.001787317,0.0005271261,0.007298468],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004121811,"threshold_uncertainty_score":0.02179849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03344005336879267,"score_gpt":0.3260512910146305,"score_spread":0.2926112376458379,"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."}}