{"id":"W2115482638","doi":"10.1023/b:dami.0000005258.31418.83","title":"Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach","year":2003,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2615,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Data mining; Computer science; Scalability; Tree (set theory); Set (abstract data type); Apriori algorithm; GSP Algorithm; Association rule learning; Trie; Tree structure; Pattern recognition (psychology); Data structure; Artificial intelligence; Database; Mathematics","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":[0.005180415,0.001300011,0.003232713,0.004975306,0.001423573,0.003417117,0.00434752,0.002591436,0.002064071],"category_scores_gemma":[0.02203915,0.001061476,0.00262189,0.006072121,0.001022005,0.006168364,0.001920203,0.002250755,0.001881533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025765,"about_ca_system_score_gemma":0.001824103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001007755,"about_ca_topic_score_gemma":0.001539642,"domain_scores_codex":[0.9948336,0.001288063,0.0005897361,0.001057398,0.001971786,0.0002594239],"domain_scores_gemma":[0.9828394,0.01182238,0.0009671408,0.001924336,0.002108966,0.0003377635],"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":[0.001031068,0.001224696,0.01331,0.001352846,0.0007791875,0.002112762,0.0004719143,0.03880866,0.01247221,0.02362365,0.008990197,0.8958228],"study_design_scores_gemma":[0.0002655924,0.0006092436,0.002345708,0.0001688598,0.0007396905,0.003678299,0.0003230342,0.8846376,0.008669051,0.0908212,0.007629762,0.000112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01554049,0.0007677517,0.9811126,0.0003627418,0.0000735874,0.0002575588,0.0005567197,0.0007541402,0.0005744072],"genre_scores_gemma":[0.09764457,0.0006919973,0.8979678,0.0002048634,0.0001523225,0.000236876,0.001938123,0.0001178523,0.001045749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005180415,"threshold_uncertainty_score":0.02739698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06223377143017436,"score_gpt":0.2906354910060366,"score_spread":0.2284017195758623,"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."}}