{"id":"W4390112344","doi":"10.1007/s10586-023-04205-5","title":"An ensemble clustering approach for modeling hidden categorization perspectives for cloud workloads","year":2023,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cluster analysis; Cloud computing; Data mining; Categorization; Workload; Conceptual clustering; Data stream clustering; Fuzzy clustering; Bottleneck; Machine learning; Artificial intelligence; CURE data clustering algorithm","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":[0.001635981,0.000770426,0.0008409959,0.001712069,0.0007824856,0.00122511,0.001440896,0.001026158,0.001093923],"category_scores_gemma":[0.003803161,0.0004004028,0.001564247,0.001805656,0.0002721136,0.001658038,0.0008319875,0.001510339,0.0004397145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009907089,"about_ca_system_score_gemma":0.0008520092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01387132,"about_ca_topic_score_gemma":0.02173282,"domain_scores_codex":[0.9992483,0.0002399318,0.0000428239,0.0001990106,0.0001547257,0.0001150807],"domain_scores_gemma":[0.9981409,0.001033331,0.0001224993,0.0002308628,0.0003949482,0.00007739094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004183766,0.0003596792,0.01224391,0.0001113151,0.0006649814,0.0002039561,0.0007374443,0.6710184,0.007821889,0.02571835,0.005867707,0.2748339],"study_design_scores_gemma":[0.00000152598,0.00001123686,0.0004729573,0.000003698507,0.00001273803,0.000009042156,0.00001673635,0.9940282,0.0001830979,0.00507349,0.0001819577,0.000005272951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05948757,0.000413647,0.9378186,0.0002400318,0.00007344655,0.00005775853,0.0004185541,0.0005618663,0.0009285394],"genre_scores_gemma":[0.7551201,0.0004258052,0.2390825,0.0001365795,0.0001557521,0.0001693129,0.001979642,0.0001742696,0.002756134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01387132,"threshold_uncertainty_score":0.02758116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03532039395177234,"score_gpt":0.2766147355894165,"score_spread":0.2412943416376442,"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."}}