{"id":"W2142996119","doi":"10.1109/icppw.2006.63","title":"PLT- Positional Lexicographic Tree: A New Structure for Mining Frequent Itemsets","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Lexicographical order; Computer science; Correctness; Data mining; Association rule learning; Representation (politics); Set (abstract data type); Tree structure; Tree (set theory); Database; Data structure; Search engine indexing; Theoretical computer science; Information retrieval; Algorithm; Mathematics; Programming language","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":[0.001669491,0.000516289,0.0008965164,0.004301268,0.0009256265,0.00184753,0.001474618,0.001100713,0.002955034],"category_scores_gemma":[0.009683285,0.0005294275,0.001033434,0.006660957,0.0008555517,0.004501751,0.001513648,0.0009839832,0.001889836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000567492,"about_ca_system_score_gemma":0.001873099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001093019,"about_ca_topic_score_gemma":0.00141367,"domain_scores_codex":[0.9986368,0.0003283716,0.0002499764,0.0002162858,0.0004912753,0.00007724272],"domain_scores_gemma":[0.9969537,0.00131351,0.0003414613,0.0005570831,0.0007332188,0.000101084],"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":[0.000508656,0.0001992049,0.008648311,0.001464031,0.0001785001,0.001163005,0.0009827892,0.02737524,0.01755578,0.1098768,0.02982764,0.80222],"study_design_scores_gemma":[0.0002623495,0.0007610163,0.003960494,0.0005445311,0.0002419177,0.006509966,0.0009095512,0.5205091,0.02912493,0.30708,0.1298929,0.0002031859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01081006,0.0005801192,0.9798107,0.0005712315,0.0001310891,0.0003700629,0.003130429,0.002452134,0.002144245],"genre_scores_gemma":[0.04200588,0.0004861833,0.9505106,0.0001775643,0.00007789407,0.0003833024,0.005248059,0.0001499427,0.000960537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004301268,"threshold_uncertainty_score":0.00988555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01153421255386396,"score_gpt":0.2403707526457563,"score_spread":0.2288365400918923,"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."}}