{"id":"W3139417388","doi":"10.1016/j.neucom.2021.02.062","title":"Automatic determining optimal parameters in multi-kernel collaborative fuzzy clustering based on dimension constraint","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Kernel (algebra); Computer science; Kernel embedding of distributions; Kernel method; Kernel principal component analysis; Variable kernel density estimation; Fuzzy clustering; Dimensionality reduction; Constrained clustering; Constraint (computer-aided design); Dimension (graph theory); Artificial intelligence; Data mining; Mathematics; Pattern recognition (psychology); CURE data clustering algorithm; Support vector machine","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":[0.001618385,0.0008035536,0.001703311,0.001009004,0.001052363,0.001650002,0.001888341,0.001531732,0.0009894685],"category_scores_gemma":[0.006881577,0.0006005822,0.001011969,0.001145859,0.0008418153,0.002185271,0.001500303,0.001102488,0.0004095669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009001829,"about_ca_system_score_gemma":0.001576758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005112281,"about_ca_topic_score_gemma":0.0041092,"domain_scores_codex":[0.9984044,0.0004369193,0.000130385,0.0004480956,0.000414644,0.0001655246],"domain_scores_gemma":[0.997715,0.0009275871,0.0002034329,0.0003073726,0.0007605267,0.00008613718],"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":[0.0006322052,0.0001679596,0.002760482,0.0003340499,0.0001786795,0.0001542286,0.0004722039,0.6158856,0.02599916,0.01697512,0.002904117,0.3335362],"study_design_scores_gemma":[0.000009332332,0.00002150086,0.0003703974,0.000009560916,0.00001784988,0.00003481912,0.00004771718,0.992413,0.003414814,0.003372881,0.0002693837,0.00001874421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02697274,0.0001900979,0.9719166,0.00004913905,0.00001613271,0.00002638421,0.00003047635,0.0002342565,0.0005642963],"genre_scores_gemma":[0.6483606,0.0001764397,0.3501423,0.00003995523,0.00002184304,0.000123888,0.0001877629,0.0001244502,0.0008228126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005112281,"threshold_uncertainty_score":0.0101651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03790629680017404,"score_gpt":0.3166096616850964,"score_spread":0.2787033648849223,"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."}}