{"id":"W2116595874","doi":"10.1007/11562214_25","title":"Document Clustering with Grouping and Chaining Algorithms","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Chaining; Cluster analysis; Document clustering; Event (particle physics); Backward chaining; Set (abstract data type); Data mining; Algorithm; Canopy clustering algorithm; Correlation clustering; Artificial intelligence; Information retrieval; Expert system; Programming language","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":[0.002877561,0.00133082,0.002258103,0.00444362,0.00185778,0.002605224,0.003377322,0.001885421,0.007136512],"category_scores_gemma":[0.008142639,0.001151933,0.00187361,0.01112775,0.0009430939,0.004225528,0.00228077,0.001734473,0.00590338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009187336,"about_ca_system_score_gemma":0.00150877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003929589,"about_ca_topic_score_gemma":0.005064665,"domain_scores_codex":[0.9972093,0.0006724074,0.0002819865,0.0007251212,0.0009873087,0.000123929],"domain_scores_gemma":[0.9957511,0.001474165,0.0001513226,0.001548775,0.0009594504,0.0001150538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001758787,0.0001258496,0.000539314,0.0002727978,0.0001406265,0.00004414071,0.0001471962,0.04224962,0.004863256,0.01513637,0.0124437,0.9238613],"study_design_scores_gemma":[0.00009934912,0.0001874921,0.0009346352,0.00007139121,0.0002118035,0.0005001649,0.0001238489,0.8108919,0.02436084,0.1196303,0.04289793,0.0000903521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002250296,0.0005286386,0.9937906,0.00006319429,0.00008708013,0.00009620543,0.0001382779,0.001649209,0.001396589],"genre_scores_gemma":[0.01486626,0.0003497569,0.9799215,0.0000347998,0.0000952577,0.00009649801,0.0005883044,0.0002555209,0.003792226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007136512,"threshold_uncertainty_score":0.02387404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436295964117985,"score_gpt":0.2329629422753225,"score_spread":0.2185999826341427,"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."}}