{"id":"W1853932977","doi":"10.1504/ijbidm.2015.071324","title":"High performance framework for mining association rules from hierarchical data cubes","year":2015,"lang":"en","type":"article","venue":"International Journal of Business Intelligence and Data Mining","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Online analytical processing; Computer science; Data cube; Association rule learning; Data mining; Dimension (graph theory); Hierarchy; Cube (algebra); Data stream mining; Granularity; Data warehouse; Overhead (engineering); Database; Theoretical computer science; Information retrieval; Programming language","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":[0.002940588,0.001302992,0.001340484,0.002500745,0.001312786,0.003156538,0.003707586,0.001013088,0.003512553],"category_scores_gemma":[0.007379833,0.0008575729,0.00177231,0.004013344,0.0007530305,0.003156574,0.002831368,0.001886728,0.002069734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023207,"about_ca_system_score_gemma":0.002949932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009798086,"about_ca_topic_score_gemma":0.01023625,"domain_scores_codex":[0.9969153,0.0005168405,0.0003667549,0.0004050109,0.001527126,0.0002689567],"domain_scores_gemma":[0.9971213,0.001091007,0.0001856939,0.0006370335,0.0007672784,0.0001976543],"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":[0.001974698,0.0009420966,0.009716022,0.00118809,0.0007121146,0.001421711,0.0008708957,0.1631617,0.02736204,0.09288199,0.05194664,0.647822],"study_design_scores_gemma":[0.000115836,0.0001361091,0.000842787,0.00003699584,0.00007048793,0.0003144754,0.0001082963,0.9360194,0.007795372,0.03516081,0.01933974,0.00005979438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005654956,0.000422356,0.9776383,0.0001910194,0.0000405167,0.0002455521,0.0007682539,0.01406496,0.0009740816],"genre_scores_gemma":[0.07707994,0.0003614117,0.9165578,0.0001436181,0.00004921283,0.0004239421,0.003947789,0.0002793175,0.001156989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009798086,"threshold_uncertainty_score":0.01948208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1335622681940514,"score_gpt":0.3590735758346469,"score_spread":0.2255113076405955,"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."}}