{"id":"W201929260","doi":"10.5220/0001703002880294","title":"AN EFFICIENT HYBRID METHOD FOR CLUSTERING PROBLEMS","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence","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":[0.0009082235,0.0009102612,0.001037393,0.001375313,0.0007902104,0.0009843755,0.001891812,0.001676703,0.004295756],"category_scores_gemma":[0.001456714,0.0006355361,0.001039333,0.001793292,0.0005476207,0.001185252,0.001348943,0.001019477,0.001057151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006058952,"about_ca_system_score_gemma":0.0007429955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002755959,"about_ca_topic_score_gemma":0.00355779,"domain_scores_codex":[0.9994645,0.0001598203,0.00002056117,0.00006338094,0.0002476601,0.00004408268],"domain_scores_gemma":[0.9993439,0.0003294589,0.00003407412,0.00007118351,0.0001838986,0.00003737995],"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":[0.0002122045,0.000112707,0.0003399687,0.000235728,0.0001752143,0.00009469134,0.000115424,0.6367537,0.01130792,0.02685866,0.006090585,0.3177031],"study_design_scores_gemma":[0.00002357531,0.00003347278,0.00008227666,0.000007273583,0.0000175538,0.00003468309,0.00001002842,0.9923459,0.0008632847,0.004345122,0.002224952,0.00001175518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005516737,0.0003057004,0.9907912,0.00008665799,0.0001084632,0.00004472187,0.00003801415,0.0003479306,0.002760596],"genre_scores_gemma":[0.1126503,0.0003486635,0.8780633,0.0001395899,0.0001104864,0.0003728028,0.0001905425,0.000219406,0.007904897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004295756,"threshold_uncertainty_score":0.0143708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05588661950587431,"score_gpt":0.3439156715610338,"score_spread":0.2880290520551595,"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."}}