{"id":"W2048032757","doi":"10.1145/1451940.1451967","title":"Efficient algorithms for stream mining of constrained frequent patterns in a limited memory environment","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Data stream mining; Data mining; Process (computing); Focus (optics); Data stream; Machine learning","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":[0.0001776572,0.0001144174,0.0001722624,0.0001108181,0.00006596172,0.00001548486,0.0004337111,0.00003853804,0.00002246237],"category_scores_gemma":[0.00001586678,0.000104748,0.00005434071,0.0001608574,0.00007310743,0.00005439325,0.0001301908,0.00004867633,0.000005757246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000405881,"about_ca_system_score_gemma":0.00005038365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001085504,"about_ca_topic_score_gemma":0.000007212952,"domain_scores_codex":[0.9988819,0.00001717989,0.0003305781,0.0003556964,0.0001844817,0.0002301664],"domain_scores_gemma":[0.9992143,0.000139427,0.00009646153,0.000455332,0.00002414855,0.00007033957],"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":[0.00002463186,0.004431713,0.009818974,0.0001363985,0.0001444118,0.000103673,0.01413591,0.03261048,0.01186437,0.02186604,0.002353589,0.9025098],"study_design_scores_gemma":[0.0008627587,0.0001368246,0.006156685,0.00002913336,0.000004671007,0.00002182825,0.0003538366,0.9865123,0.005347842,0.0000400769,0.0003593428,0.0001747248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3373438,0.00001876394,0.6616148,0.0002805048,0.00004223767,0.0002916185,0.00008118602,0.00004091875,0.0002861869],"genre_scores_gemma":[0.5780703,0.00001097009,0.421607,0.00006356026,0.0000156877,0.000105162,0.00002350837,0.000005734323,0.00009801288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9539018,"threshold_uncertainty_score":0.4271499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03478426409996017,"score_gpt":0.2492112962609861,"score_spread":0.2144270321610259,"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."}}