{"id":"W146732267","doi":"","title":"Clustering using an Autoassociator: A Case Study in Network Event Correlation.","year":2005,"lang":"en","type":"article","venue":"IASTED PDCS","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cluster analysis; Computer science; Novelty; Artificial intelligence; Data mining; Artificial neural network; Event (particle physics); Feedforward neural network; Feature (linguistics); Task (project management); Correlation clustering; Correlation; Machine learning; Pattern recognition (psychology); Mathematics; Engineering","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":[0.003162557,0.0005563677,0.0007671688,0.001383858,0.001278433,0.001247497,0.001320877,0.00219224,0.00113064],"category_scores_gemma":[0.01059067,0.0002977889,0.0006751148,0.002905981,0.001007639,0.00179303,0.001069151,0.001123977,0.0003609379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008437447,"about_ca_system_score_gemma":0.0005815485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00406285,"about_ca_topic_score_gemma":0.00609549,"domain_scores_codex":[0.9979628,0.0009105012,0.00009780538,0.0003174236,0.0005634181,0.0001480902],"domain_scores_gemma":[0.9919668,0.005192143,0.0005312008,0.001003197,0.001043607,0.0002630342],"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":[0.001729056,0.001134966,0.07896905,0.001232577,0.0006642591,0.01587867,0.004892854,0.3806051,0.02862405,0.04227133,0.0147799,0.4292182],"study_design_scores_gemma":[0.0001030693,0.000564763,0.0211677,0.00006975896,0.000152972,0.007546625,0.001474643,0.8864442,0.03644807,0.02483694,0.02106528,0.0001260833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6063398,0.001370229,0.3802459,0.001680168,0.0001343244,0.0003049125,0.0004532675,0.001394664,0.008076681],"genre_scores_gemma":[0.8738616,0.0004977529,0.1223656,0.0001741023,0.00006434858,0.00009527016,0.0003079501,0.0001104768,0.002522804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00406285,"threshold_uncertainty_score":0.01672542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03270833436532282,"score_gpt":0.2903363437473334,"score_spread":0.2576280093820106,"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."}}