{"id":"W4362589576","doi":"10.1007/s10115-023-01852-3","title":"Mining frequent generators and closures in data streams with FGC-Stream","year":2023,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Sliding window protocol; Data stream mining; Data mining; Exploit; Association rule learning; Factoring; Data stream; STREAMS; Window (computing); Computer security","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":[0.0003667546,0.00008717779,0.0001119867,0.0001827512,0.00010405,0.0003885672,0.0003309895,0.00003557676,4.612437e-7],"category_scores_gemma":[0.00001467317,0.00006802668,0.000004679357,0.0005316873,0.00002518972,0.002877244,0.0002797249,0.00004713395,0.00004466475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001189304,"about_ca_system_score_gemma":0.0000478068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008655433,"about_ca_topic_score_gemma":0.00005927492,"domain_scores_codex":[0.9992988,0.0000216086,0.0002578042,0.0001757166,0.0001069312,0.0001391213],"domain_scores_gemma":[0.9993071,0.00004574659,0.00007871075,0.0004544417,0.00005060718,0.00006339419],"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":[0.000004708584,0.00005303343,0.02238384,0.0003309012,0.00004431275,0.00000621266,0.0189201,0.0002092812,0.00004159533,0.05986639,0.04724633,0.8508933],"study_design_scores_gemma":[0.0004589451,0.00004867652,0.008916724,0.0001416269,0.000004690011,0.00003047721,0.00185125,0.7736908,0.00004428518,0.00002026305,0.2146015,0.0001907896],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8919629,0.002778384,0.08141652,0.0006923502,0.0007899681,0.001067041,0.0004584197,0.0007273995,0.02010703],"genre_scores_gemma":[0.9914883,0.0003143886,0.007079957,0.00004922216,0.0001075763,0.00009803703,0.0006304067,0.000007404275,0.0002247435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8507025,"threshold_uncertainty_score":0.3746962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02936375096337532,"score_gpt":0.2667857281974172,"score_spread":0.2374219772340419,"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."}}