{"id":"W2151953639","doi":"10.1109/tkde.2005.166","title":"Fast algorithms for frequent itemset mining using FP-trees","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":552,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Data mining; Traverse; Association rule learning; Algorithm; Tree (set theory); Data structure; Tree structure; Trie; Prefix; Binary tree; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001941877,0.0001773885,0.0001578308,0.0001621162,0.0002377558,0.000176843,0.0007163907,0.00005733486,0.000004527164],"category_scores_gemma":[0.000005707857,0.0001836319,0.0000379477,0.0002658083,0.00001941717,0.0009323033,0.00002110892,0.0001193066,0.00001428661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004174819,"about_ca_system_score_gemma":0.00003867082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000153798,"about_ca_topic_score_gemma":0.00003306532,"domain_scores_codex":[0.9988756,0.000007874056,0.0002304932,0.0005226892,0.00009284649,0.000270515],"domain_scores_gemma":[0.9988046,0.0001390864,0.00003547944,0.0008561587,0.0000432712,0.0001214113],"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":[0.00000229816,0.0001532455,0.000002270808,0.00003794017,0.00005037117,0.000001234929,0.0005487343,0.0327771,0.00263042,0.0004759213,0.0008216894,0.9624988],"study_design_scores_gemma":[0.0002649273,0.00003324448,0.00001543196,0.00005872684,0.00002368727,0.00002060261,0.00002952387,0.9620176,0.003079309,0.000006797861,0.03423934,0.0002107866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001680434,0.0003745414,0.9963653,0.0001203766,0.0003658569,0.0001703619,0.0006825803,0.0001945859,0.0000459653],"genre_scores_gemma":[0.1133055,0.00007764086,0.8859453,0.00003875242,0.0002993993,0.00007474193,0.00008503442,0.00002955934,0.0001440631],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.962288,"threshold_uncertainty_score":0.7488291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05517782314487753,"score_gpt":0.3067297556925058,"score_spread":0.2515519325476283,"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."}}