{"id":"W54192826","doi":"10.1007/978-3-642-32584-7_17","title":"A Fast Algorithm for Frequent Itemset Mining Using Patricia* Structures","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Trie; Tree traversal; Prefix; Computer science; Tree (set theory); Interval tree; Data structure; Data mining; Algorithm; Tree structure; Theoretical computer science; Segment tree; Fractal tree index; Node (physics); Search tree; Binary tree; Mathematics; Search algorithm; Combinatorics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007562794,0.0005762348,0.0005498345,0.0007327895,0.0005083423,0.000756365,0.003257318,0.0003198209,0.00001231607],"category_scores_gemma":[0.0000502953,0.000535436,0.0001638141,0.0006313996,0.0004052103,0.0007899712,0.001224171,0.0004877027,0.0000105859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003230039,"about_ca_system_score_gemma":0.0004810838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005508636,"about_ca_topic_score_gemma":0.00001864298,"domain_scores_codex":[0.9961619,0.00002082845,0.0005941238,0.001518697,0.0007782456,0.000926166],"domain_scores_gemma":[0.9971427,0.0004604877,0.0004242237,0.001450145,0.0002712412,0.0002511548],"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":[6.651346e-7,0.00001805662,0.000008883901,0.00001834403,0.00001125491,0.000007524904,0.000483175,0.003648687,0.00007108713,0.006548402,0.00003627794,0.9891477],"study_design_scores_gemma":[0.000226632,0.00008220811,0.00003218415,0.0001626295,0.00002077128,0.0000890845,6.328273e-7,0.9502805,0.0004448577,0.04310395,0.004883981,0.0006724942],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004165455,0.0009886498,0.9960335,0.0001765669,0.001619337,0.0006037834,0.0001738912,0.0001474676,0.0002150891],"genre_scores_gemma":[0.002212703,0.00002684221,0.9957531,0.0005675745,0.001234263,0.00003122249,0.00004536588,0.00004681375,0.00008211592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9884751,"threshold_uncertainty_score":0.9997097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03314512769457269,"score_gpt":0.2800462179408509,"score_spread":0.2469010902462782,"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."}}