{"id":"W3211395167","doi":"10.1109/access.2021.3126854","title":"KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Institute for Information and Communications Technology Promotion; National Research Foundation of Korea","keywords":"Cluster analysis; Spectral clustering; CURE data clustering algorithm; Computer science; Correlation clustering; k-nearest neighbors algorithm; Pattern recognition (psychology); Nearest-neighbor chain algorithm; Canopy clustering algorithm; Graph; Algorithm; Noise (video); Artificial intelligence; Data mining; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000245035,0.0002764661,0.0003030385,0.0001924425,0.0003155506,0.001232436,0.002292697,0.0001070639,0.00004633653],"category_scores_gemma":[0.00007560486,0.0002977883,0.0001114467,0.001319466,0.00008586234,0.002267208,0.001756577,0.0004432355,0.00004528019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002674706,"about_ca_system_score_gemma":0.0003164476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002181283,"about_ca_topic_score_gemma":0.00006782902,"domain_scores_codex":[0.9970606,0.00007240158,0.0003594222,0.0008891533,0.0007248846,0.0008935516],"domain_scores_gemma":[0.9981031,0.0001369685,0.0001099745,0.001100563,0.0002840685,0.000265257],"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":[0.00002849659,0.0006288546,0.001199227,0.0002115099,0.0002073937,0.003546102,0.00119172,0.2101781,0.1306759,0.001803252,0.0003891794,0.6499403],"study_design_scores_gemma":[0.0005415098,0.0000285714,0.001276368,0.00006651792,0.00000566764,0.0003731395,0.00002368332,0.9445028,0.05140375,0.0005530468,0.0008359827,0.000388909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02019568,0.0001111651,0.9763848,0.0003216729,0.001750138,0.0001557115,0.00001266871,0.0002697151,0.000798458],"genre_scores_gemma":[0.1689129,0.00004304857,0.8288617,0.0004427172,0.0009124139,0.00002325748,0.000006086439,0.00007342902,0.0007244577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7343248,"threshold_uncertainty_score":0.9999474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08371092394036551,"score_gpt":0.3714442137006123,"score_spread":0.2877332897602468,"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."}}