{"id":"W2566763598","doi":"10.5539/cis.v10n1p23","title":"The Impact of the Pattern-Growth Ordering on the Performances of Pattern Growth-Based Sequential Pattern Mining Algorithms","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Pruning; Prefix; Field (mathematics); Set (abstract data type); Algorithm; Sequential Pattern Mining; Sequence (biology); Web mining; Pattern search; Tree (set theory); Suffix tree; Pattern recognition (psychology); Data structure; Artificial intelligence; Mathematics; Web page","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008894317,0.0001277344,0.0001106309,0.0001048828,0.0006304542,0.0003869061,0.001893106,0.0000228651,0.000004218079],"category_scores_gemma":[0.00004851856,0.00004978458,0.00006988885,0.0006228591,0.0005512786,0.002551533,0.0005303354,0.00007852547,0.000007954968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000312439,"about_ca_system_score_gemma":0.0001525812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001049444,"about_ca_topic_score_gemma":0.000001608576,"domain_scores_codex":[0.9986785,0.00003921283,0.0003863446,0.0001804906,0.000467489,0.0002479749],"domain_scores_gemma":[0.9984366,0.000292453,0.000338939,0.0005853811,0.0002984765,0.00004812318],"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":[0.000001240351,0.00001396768,0.006708453,0.00001116033,0.000007382101,5.837762e-8,0.001133546,0.0001357,0.0002131486,0.0008515966,0.0002872409,0.9906365],"study_design_scores_gemma":[0.0003154313,0.0001592846,0.1226271,0.0001453482,0.00000323918,0.000007239627,0.0000625742,0.8665041,0.009665444,0.00008604299,0.0002930177,0.0001311245],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2177863,0.000007620149,0.7801319,0.001496516,0.0002560625,0.0001444739,0.00002566681,0.00002045686,0.0001309987],"genre_scores_gemma":[0.9978881,0.00002411572,0.001675521,0.0003513789,0.00003844363,0.00001519624,0.000001536038,0.000002649486,0.000003094165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905054,"threshold_uncertainty_score":0.4849008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179870359795963,"score_gpt":0.2576322095092082,"score_spread":0.2396451735296119,"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."}}