{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005375003,0.00257738,0.002355215,0.005952595,0.002237939,0.002918095,0.003249121,0.002132346,0.005925441],"category_scores_gemma":[0.01842988,0.0009902064,0.002908827,0.01249242,0.002107578,0.004704318,0.004339161,0.00391329,0.00301008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001336112,"about_ca_system_score_gemma":0.002590618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002942312,"about_ca_topic_score_gemma":0.003081787,"domain_scores_codex":[0.9891651,0.003977937,0.0007816641,0.002444285,0.0032921,0.0003388532],"domain_scores_gemma":[0.9919435,0.003356928,0.0006361281,0.002329498,0.001575132,0.0001588444],"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":[0.0003539117,0.0002392073,0.002766054,0.001564502,0.0007248315,0.0004305415,0.00108513,0.06390357,0.009194709,0.1477842,0.01842519,0.7535281],"study_design_scores_gemma":[0.00006922565,0.0001517608,0.001543775,0.0001648066,0.0001855711,0.0007549088,0.0003024196,0.6809596,0.01287672,0.234316,0.06850021,0.0001749764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009133389,0.0003880927,0.9967086,0.0001382165,0.00007008675,0.0001215392,0.0001258526,0.0009001699,0.0006340228],"genre_scores_gemma":[0.02365411,0.0004507854,0.9734762,0.0001405018,0.0000987626,0.0005240075,0.0006247838,0.0002254868,0.0008053708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005952595,"threshold_uncertainty_score":0.02842605,"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."}}