{"id":"W1997024687","doi":"10.4018/jdm.2006070102","title":"A Framework for Efficient Association Rule Mining in XML Data","year":2006,"lang":"en","type":"article","venue":"Journal of Database Management","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; XML; Efficient XML Interchange; Association rule learning; Data mining; XML validation; Streaming XML; XML database; Generalization; Constraint (computer-aided design); Information retrieval; Task (project management); XML Schema (W3C); XML Encryption; World Wide Web","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":[0.01244393,0.001483352,0.002376189,0.00533114,0.001242001,0.003444904,0.004534073,0.001855807,0.002599518],"category_scores_gemma":[0.02005422,0.001524096,0.004029792,0.005507602,0.001166867,0.004643674,0.003330115,0.003454239,0.002816868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007521968,"about_ca_system_score_gemma":0.002074591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002111252,"about_ca_topic_score_gemma":0.002935933,"domain_scores_codex":[0.9910197,0.003721913,0.001269657,0.001126203,0.002658103,0.0002044052],"domain_scores_gemma":[0.9919825,0.004614539,0.0006473734,0.001677945,0.0008913914,0.0001862873],"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":[0.0002897801,0.0004019015,0.002452819,0.00182858,0.0009407421,0.001651217,0.0006619276,0.1254844,0.009029647,0.2009569,0.02262739,0.6336747],"study_design_scores_gemma":[0.0001640835,0.0002467885,0.0007738914,0.0004168251,0.0001863347,0.002208776,0.0001837545,0.7230161,0.007334835,0.1851931,0.08013634,0.0001392393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002473354,0.0001778133,0.9979998,0.0001091931,0.00001692981,0.00009161007,0.0001516081,0.00108685,0.0001187701],"genre_scores_gemma":[0.004967404,0.0002446061,0.9936956,0.00008154963,0.00002950857,0.000255065,0.0004761175,0.00004433912,0.0002058547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01244393,"threshold_uncertainty_score":0.06581056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03398456354941731,"score_gpt":0.3067758063708545,"score_spread":0.2727912428214372,"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."}}