{"id":"W1999518899","doi":"10.1007/s10618-014-0398-2","title":"Mining outlying aspects on numeric data","year":2015,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Subspace topology; Object (grammar); Outlier; Computer science; Data mining; Set (abstract data type); Rank (graph theory); Heuristic; Curse of dimensionality; Measure (data warehouse); Data set; Mathematics; Artificial intelligence; Combinatorics","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":[0.002479302,0.0007806382,0.001220317,0.005686093,0.001044109,0.002504597,0.00141197,0.0009756576,0.0007559061],"category_scores_gemma":[0.02586422,0.0004243968,0.0009766672,0.006963037,0.001159593,0.005039849,0.001985298,0.001819175,0.0003455668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006256874,"about_ca_system_score_gemma":0.001273179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001658244,"about_ca_topic_score_gemma":0.002448636,"domain_scores_codex":[0.9962114,0.000426325,0.0005044695,0.0006659655,0.001935187,0.0002566552],"domain_scores_gemma":[0.9726827,0.0121404,0.005294499,0.004979481,0.004311825,0.0005910455],"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":[0.0008550968,0.0003627724,0.4227242,0.0006895618,0.0004446939,0.004577211,0.002760668,0.02138935,0.02432215,0.02675872,0.005293347,0.4898223],"study_design_scores_gemma":[0.00006128171,0.0005341182,0.1254985,0.0004401062,0.0007220343,0.006281441,0.003961196,0.5935636,0.0394789,0.2039662,0.0253551,0.0001376069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6128554,0.00132491,0.3777225,0.001119199,0.0001301942,0.0001490754,0.002044903,0.001966867,0.002686969],"genre_scores_gemma":[0.8387077,0.0006391647,0.1554639,0.0001726359,0.0001884474,0.00008904442,0.003435644,0.0002063196,0.001097132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005686093,"threshold_uncertainty_score":0.01311195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1626365810308897,"score_gpt":0.3484467786911583,"score_spread":0.1858101976602685,"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."}}