{"id":"W4240428965","doi":"10.1007/3-540-36175-8_40","title":"HOT: Hypergraph-Based Outlier Test for Categorical Data","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Categorical variable; Computer science; Outlier; Data mining; Robustness (evolution); Linear subspace; Anomaly detection; Curse of dimensionality; Missing data; Computation; Hypergraph; Cluster analysis; Artificial intelligence; Algorithm; Machine learning; Mathematics","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":[0.002354649,0.0009833309,0.001872109,0.004455979,0.0008381951,0.001410134,0.002803182,0.001771401,0.003985288],"category_scores_gemma":[0.01172048,0.0004991455,0.001086276,0.00338057,0.001047792,0.003118437,0.002537119,0.001702625,0.001523525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000580152,"about_ca_system_score_gemma":0.0009866721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001603941,"about_ca_topic_score_gemma":0.001982473,"domain_scores_codex":[0.9966651,0.001007192,0.0001966821,0.0005454327,0.00135928,0.0002263427],"domain_scores_gemma":[0.9907268,0.004731575,0.0006630217,0.002091417,0.001362076,0.0004250459],"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":[0.001621503,0.000433441,0.01948859,0.0003244008,0.0004922037,0.0004629832,0.0002145252,0.06227322,0.02398129,0.01106638,0.02548748,0.854154],"study_design_scores_gemma":[0.0001250522,0.0004254288,0.006802814,0.00002965591,0.00009708835,0.0005836828,0.0001484836,0.9240713,0.01750126,0.04520027,0.00494488,0.00007010399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03300986,0.0002363185,0.9517959,0.0002822098,0.0001471312,0.0001413402,0.00112641,0.0124457,0.0008150723],"genre_scores_gemma":[0.3658932,0.0001862967,0.6233563,0.0002663672,0.0003125091,0.0003288999,0.005005126,0.001113444,0.003537852],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004455979,"threshold_uncertainty_score":0.01333213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03384550495253059,"score_gpt":0.2738144117525889,"score_spread":0.2399689068000583,"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."}}