{"id":"W1556829956","doi":"10.1007/978-3-540-25966-4_14","title":"A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Computer science; Conditional probability; Cluster analysis; Probability distribution; Entropy (arrow of time); Conditional probability distribution; Data mining; Divergence (linguistics); Expectation–maximization algorithm; Statistical model; Artificial intelligence; Mathematics; Statistics","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":[0.001244723,0.0005448048,0.0004971051,0.000980933,0.0003865684,0.0008458737,0.002996804,0.0003213703,0.0000030959],"category_scores_gemma":[0.0004410009,0.0004916029,0.0001502455,0.0004822466,0.0008188618,0.001767957,0.001442371,0.0006492129,0.00001256843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001144717,"about_ca_system_score_gemma":0.001629462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001312785,"about_ca_topic_score_gemma":0.0000170792,"domain_scores_codex":[0.9959444,0.00003189579,0.0006461711,0.001104975,0.001363623,0.0009088996],"domain_scores_gemma":[0.9969434,0.0005377493,0.0003029127,0.001306569,0.0007172282,0.0001921127],"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":[0.00001244611,0.000009127132,3.768406e-7,0.000121672,0.000005009892,0.000003170868,0.0006823979,0.7599996,0.00004017239,0.04043506,0.000001222206,0.1986898],"study_design_scores_gemma":[0.0002675229,0.00007749817,7.213133e-7,0.000274684,0.00000467869,0.00003278678,1.156374e-7,0.5601233,0.0002718726,0.4385826,0.00005160135,0.0003127177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005889371,0.000112268,0.9967858,0.0003913164,0.0005888482,0.001337647,0.00001343334,0.0001754085,0.0005363846],"genre_scores_gemma":[0.05929951,0.00001657194,0.939643,0.0006869106,0.00018825,0.00004893432,0.000006274488,0.0000409305,0.00006956458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3981475,"threshold_uncertainty_score":0.9997535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03497427916053233,"score_gpt":0.2921393253852348,"score_spread":0.2571650462247025,"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."}}