{"id":"W2109450366","doi":"10.1109/icdm.2006.94","title":"Latent Dirichlet Co-Clustering","year":2006,"lang":"en","type":"article","venue":"Proceedings","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Latent Dirichlet allocation; Cluster analysis; Document clustering; Computer science; Correlation clustering; Markov chain Monte Carlo; Dirichlet distribution; Biclustering; Canopy clustering algorithm; CURE data clustering algorithm; Topic model; Hierarchical Dirichlet process; Mixture model; Artificial intelligence; Brown clustering; Data mining; Mathematics; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00548035,0.001582597,0.00298121,0.005154615,0.002421327,0.00449756,0.004628793,0.003276709,0.006224778],"category_scores_gemma":[0.01945014,0.001384366,0.00359853,0.007118186,0.002473837,0.005018378,0.004017146,0.004441113,0.003923031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003139194,"about_ca_system_score_gemma":0.002966048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00766685,"about_ca_topic_score_gemma":0.01118138,"domain_scores_codex":[0.990889,0.004413835,0.0003812521,0.0018307,0.001943259,0.0005419627],"domain_scores_gemma":[0.9915765,0.004938324,0.0005461607,0.001696432,0.0009781398,0.0002645404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001447477,0.0001738063,0.002665545,0.0004271239,0.0003688863,0.0002914669,0.001127673,0.2601039,0.0018954,0.5789536,0.01295831,0.1408896],"study_design_scores_gemma":[0.0000195143,0.0000172961,0.0004810109,0.00006267682,0.0000537808,0.0001926066,0.0000888474,0.6852814,0.0008102514,0.3009903,0.0119439,0.00005837299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001925994,0.0004410213,0.9949936,0.0002485453,0.00005256886,0.00007207527,0.0002483951,0.0002837466,0.001734137],"genre_scores_gemma":[0.1654104,0.001640912,0.8169096,0.0004153486,0.0003298174,0.0009744634,0.002533703,0.0005217638,0.01126414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00766685,"threshold_uncertainty_score":0.02898318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379233141525034,"score_gpt":0.2527912353654586,"score_spread":0.2389989039502082,"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."}}