{"id":"W1556196612","doi":"10.3233/ida-2011-0501","title":"Mining sequential patterns with extensible knowledge representation","year":2011,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data mining; Knowledge extraction; Representation (politics); Set (abstract data type); Pruning; Knowledge representation and reasoning; Association rule learning; K-optimal pattern discovery; Apriori algorithm; A priori and a posteriori; Machine learning; Artificial intelligence","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":[0.002235093,0.0005923841,0.0006899339,0.003412938,0.0003878365,0.002141753,0.001634506,0.0007091454,0.001936173],"category_scores_gemma":[0.01044613,0.0004606525,0.001175604,0.003976878,0.0005520749,0.005211531,0.001872405,0.001014133,0.000581191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811658,"about_ca_system_score_gemma":0.0007901784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001661055,"about_ca_topic_score_gemma":0.001978997,"domain_scores_codex":[0.9983642,0.0003489731,0.0003772831,0.0003263251,0.0005003909,0.00008293834],"domain_scores_gemma":[0.9958977,0.001833476,0.0004829148,0.001250113,0.0004542058,0.00008158022],"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":[0.0003991336,0.0003995098,0.008794835,0.000587207,0.0003257275,0.001328026,0.0006827902,0.07430415,0.009762631,0.06176614,0.005222535,0.8364274],"study_design_scores_gemma":[0.00008067258,0.0001911124,0.002229824,0.000238981,0.0002464168,0.0009410848,0.0002585297,0.7941144,0.01031503,0.1743866,0.01694213,0.00005525837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04141129,0.0005938362,0.9503316,0.0004484146,0.00005389416,0.0002656642,0.001480731,0.003188071,0.002226569],"genre_scores_gemma":[0.1798047,0.0007697383,0.8127372,0.0001781354,0.00004334829,0.0002724838,0.004763094,0.0000833661,0.001348038],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003412938,"threshold_uncertainty_score":0.0118205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.182989932994737,"score_gpt":0.3504488406659183,"score_spread":0.1674589076711813,"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."}}