{"id":"W2128933128","doi":"10.1109/tkde.2008.38","title":"Simultaneous Pattern and Data Clustering for Pattern Cluster Analysis","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data mining; Cluster analysis; Categorical variable; Relation (database); Cluster (spacecraft); Data set; Set (abstract data type); Pattern recognition (psychology); Consensus clustering; Measure (data warehouse); Artificial intelligence; Fuzzy clustering; CURE data clustering algorithm; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001813423,0.0001793191,0.0002126531,0.0002071204,0.0002621051,0.0001344155,0.001130899,0.00005526418,0.000003524892],"category_scores_gemma":[0.00001035234,0.0001797189,0.00003147147,0.0003646181,0.00002770916,0.0007536913,0.0001060719,0.0001342254,0.000008439316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001337328,"about_ca_system_score_gemma":0.00001837376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000531368,"about_ca_topic_score_gemma":0.000173749,"domain_scores_codex":[0.9986657,0.00001147176,0.0002194205,0.0007784782,0.00009137484,0.0002335386],"domain_scores_gemma":[0.9975773,0.0003515846,0.00003332007,0.001879468,0.0000352214,0.0001231342],"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":[0.000003652913,0.000115368,0.00004601441,0.00008470062,0.0002774402,0.000009875062,0.0005666926,0.0234435,0.0001046683,0.000006353896,0.0006013871,0.9747403],"study_design_scores_gemma":[0.0002960615,0.00003270895,0.0001220898,0.00002281983,0.0001306145,0.00004300318,0.00001013623,0.9912726,0.000101866,0.000001467904,0.007755422,0.0002112155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002079815,0.0002102385,0.9953512,0.0001335435,0.0001888187,0.0001714565,0.001713836,0.0001401918,0.00001088752],"genre_scores_gemma":[0.9283933,0.0003045149,0.07050336,0.00008659467,0.00009904372,0.00004571191,0.0004347808,0.00002441043,0.0001083032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9745291,"threshold_uncertainty_score":0.7328723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03944602358900941,"score_gpt":0.2853976676282162,"score_spread":0.2459516440392067,"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."}}