{"id":"W1561886765","doi":"10.1007/978-3-540-30076-2_22","title":"Novel Clustering Approach that Employs Genetic Algorithm with New Representation Scheme and Multiple Objectives","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Cluster analysis; Partition (number theory); Scheme (mathematics); Encoding (memory); Algorithm; Graph partition; Graph; Set (abstract data type); Theoretical computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000363374,0.0006466768,0.0005637726,0.0008225614,0.0003439238,0.0008339402,0.002220581,0.0002672296,0.000003559751],"category_scores_gemma":[0.00007902767,0.0005760062,0.0000733646,0.0007622688,0.0008594554,0.001058269,0.002434121,0.0009426458,0.000005739058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005062191,"about_ca_system_score_gemma":0.0007804301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002672892,"about_ca_topic_score_gemma":0.00006472266,"domain_scores_codex":[0.9948515,0.00002546028,0.0003957931,0.002417327,0.001442086,0.0008678432],"domain_scores_gemma":[0.9973884,0.0003647156,0.0002581272,0.001426304,0.0002175479,0.0003449628],"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":[0.00001157516,0.00003120789,0.000164904,0.00005285268,0.00002009977,0.00005619204,0.001241552,0.2858587,0.0003683842,0.0002174868,5.826607e-7,0.7119765],"study_design_scores_gemma":[0.001050493,0.0002465701,0.001656275,0.0003713059,0.000005983858,0.0005252354,0.000001617923,0.9808877,0.001365768,0.01314226,0.00002462906,0.0007222107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001050977,0.0004229108,0.9976043,0.0001807415,0.000355929,0.0007500972,0.000004856723,0.0002175262,0.0003586008],"genre_scores_gemma":[0.01462194,0.00005873942,0.9845114,0.0001598843,0.0003148098,0.00001452868,0.000004706445,0.00006480818,0.0002492162],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7112542,"threshold_uncertainty_score":0.9996691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03641534816727335,"score_gpt":0.2810127521842175,"score_spread":0.2445974040169442,"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."}}