{"id":"W2114848212","doi":"10.1016/j.neucom.2011.08.032","title":"Clustering in applications with multiple data sources—A mutual subspace clustering approach","year":2012,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear subspace; Cluster analysis; Computer science; Subspace topology; Data mining; Clustering high-dimensional data; Set (abstract data type); Data set; Data point; Correlation clustering; Pattern recognition (psychology); Cluster (spacecraft); Artificial intelligence; Mathematics","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":[0.002713557,0.0007145287,0.001326828,0.001632988,0.001328497,0.001875939,0.002146146,0.001316361,0.00131922],"category_scores_gemma":[0.006250653,0.0006478633,0.001534514,0.002414639,0.001051551,0.003135549,0.003488389,0.001575161,0.0005897054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005753244,"about_ca_system_score_gemma":0.001361314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001737509,"about_ca_topic_score_gemma":0.002543845,"domain_scores_codex":[0.9968227,0.001270336,0.0001670319,0.0004531705,0.001139554,0.000147092],"domain_scores_gemma":[0.9978479,0.0007961255,0.0002080395,0.0004025662,0.0006507899,0.0000946203],"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":[0.0002905839,0.0002502557,0.002819224,0.0004758702,0.000563913,0.0002375355,0.0007676549,0.3585379,0.01249251,0.1292457,0.005095319,0.4892235],"study_design_scores_gemma":[0.000007743885,0.00005746302,0.0006456187,0.00001927311,0.00003694553,0.0001160153,0.0001062974,0.9624276,0.002161046,0.03152563,0.002864421,0.00003195722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003360179,0.0002611433,0.9954277,0.0001238581,0.00002136992,0.00002722643,0.00001932715,0.00009884546,0.0006602478],"genre_scores_gemma":[0.2371994,0.0009121241,0.7578861,0.0001227019,0.0002273826,0.0002307661,0.0002341311,0.0001418315,0.003045576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002713557,"threshold_uncertainty_score":0.01435083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05723579266362643,"score_gpt":0.3059612205570976,"score_spread":0.2487254278934711,"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."}}