{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000589759,0.000117961,0.0001434118,0.0001058266,0.0002412841,0.0001254575,0.0002367336,0.00008481311,0.00002106519],"category_scores_gemma":[0.0000147654,0.0001278157,0.00003514158,0.0006698772,0.000009576171,0.0007740908,0.0001832914,0.000242049,0.00001843026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002108195,"about_ca_system_score_gemma":0.00004265541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004326838,"about_ca_topic_score_gemma":0.006278821,"domain_scores_codex":[0.9986115,0.0002469725,0.0003250892,0.0003066325,0.0002133441,0.0002964488],"domain_scores_gemma":[0.9993691,0.0000547627,0.0001185453,0.0003321294,0.00004251158,0.00008297223],"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":[0.000008849881,0.0003902987,0.005545123,0.000002750861,0.0000125949,0.0004614863,0.01082241,0.9040783,0.00001399282,0.000134419,0.0001035322,0.07842622],"study_design_scores_gemma":[0.0004745808,0.0001410605,0.002723542,0.00002412441,0.000006203783,0.0005186262,0.0005686252,0.9947687,0.000005530104,0.00008261792,0.0005402886,0.0001461125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.729156,0.00005496065,0.2697231,0.00004637291,0.0005959572,0.0002231646,3.003043e-7,0.0001178298,0.00008236298],"genre_scores_gemma":[0.986634,0.000002281327,0.01274694,0.0001461364,0.0004237104,0.000008276366,6.605507e-7,0.00000918052,0.00002883073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.257478,"threshold_uncertainty_score":0.5212175,"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."}}