{"id":"W2513061279","doi":"10.1007/s10115-016-0986-0","title":"EFIM: a fast and memory efficient algorithm for high-utility itemset mining","year":2016,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":239,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Computer science; Key (lock); Data mining; Projection (relational algebra); Database transaction; Task (project management); Tree (set theory); Algorithm; High memory; Database; Mathematics; Parallel computing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000548853,0.0001143918,0.0001602475,0.0001092999,0.0002194931,0.0002770021,0.000214128,0.00005614005,0.000001484387],"category_scores_gemma":[0.00003823084,0.00007865322,0.00002025423,0.000163047,0.00004770574,0.001459292,0.0001527072,0.00002957216,0.00004823738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002252444,"about_ca_system_score_gemma":0.00003956776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001762827,"about_ca_topic_score_gemma":0.000001267471,"domain_scores_codex":[0.9991227,0.00002595643,0.0003644195,0.0001936656,0.0001101053,0.0001831537],"domain_scores_gemma":[0.999092,0.00017271,0.0001378472,0.0003033266,0.0001906559,0.0001034221],"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":[0.000001081535,0.00001394249,0.00002219216,0.00005725368,0.000006597157,6.281721e-8,0.00161845,0.000001581604,0.00001966127,0.00993299,0.003538189,0.984788],"study_design_scores_gemma":[0.0009723359,0.00007488783,0.001319578,0.0001437574,0.000008045311,0.00002787545,0.000555233,0.7896622,0.000267569,0.00005650237,0.2066921,0.0002198322],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006712245,0.0003578088,0.9895452,0.0001816323,0.00042066,0.0004128819,0.0002185038,0.0001046645,0.002046431],"genre_scores_gemma":[0.9233494,0.00008428992,0.07429645,0.0001218442,0.0003272239,0.0005772162,0.0001028264,0.00001280115,0.001127884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9845682,"threshold_uncertainty_score":0.3207385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286533636610831,"score_gpt":0.2381222319819108,"score_spread":0.2252568956158025,"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."}}