{"id":"W2591701439","doi":"","title":"On efficient and effective association rule mining from XML data","year":2004,"lang":"en","type":"article","venue":"University of Southern Queensland ePrints (University of Southern Queensland)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Association rule learning; XML; Generalization; Data mining; Tree (set theory); Automatic summarization; XML Schema (W3C); Metric (unit); Document Structure Description; Constraint (computer-aided design); Information retrieval; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043837,0.0002062819,0.0003684701,0.0001760383,0.0003622069,0.00003464736,0.00117583,0.0001710297,0.00005885001],"category_scores_gemma":[0.00005134521,0.0002438209,0.0000913034,0.0002720422,0.0001878558,0.0001984757,0.0009015036,0.0001726822,0.0003685106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280468,"about_ca_system_score_gemma":0.00008262174,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01015424,"about_ca_topic_score_gemma":0.0006164581,"domain_scores_codex":[0.9983566,0.0001031257,0.000150241,0.0007270748,0.0003903361,0.0002725592],"domain_scores_gemma":[0.9980463,0.0002766146,0.0004077397,0.0009807113,0.0001421565,0.0001464112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00147669,0.003075863,0.2845939,0.0003161478,0.002968484,0.0005144285,0.4866772,0.009088282,0.002166948,0.01410989,0.002198422,0.1928138],"study_design_scores_gemma":[0.03496739,0.0011339,0.3359261,0.002253906,0.001424955,0.00003902078,0.403712,0.170419,0.0007404331,0.03472532,0.01041824,0.004239718],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8715187,0.00002772769,0.1241056,0.0006238535,0.00004483897,0.0002151904,0.002824751,0.00008766804,0.0005516345],"genre_scores_gemma":[0.9669748,0.0000315574,0.03225604,0.00002664074,0.00002782703,8.758246e-8,0.0001793073,0.00001480342,0.0004889182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.188574,"threshold_uncertainty_score":0.9964373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045940233434889,"score_gpt":0.1898858021987238,"score_spread":0.1794263998643749,"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."}}