{"id":"W2023420371","doi":"10.5539/mas.v3n2p75","title":"Genetic Algorithm for Document Clustering with Simultaneous and Ranked Mutation","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Mutation; Mutation rate; Computer science; Genetic algorithm; Population; Similarity (geometry); Local optimum; Operator (biology); Chromosome; Algorithm; Data mining; Correlation clustering; Document clustering; Artificial intelligence; Machine learning; Genetics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009313169,0.000904929,0.001177148,0.001574669,0.0009299031,0.0009176203,0.001586497,0.001115217,0.00123461],"category_scores_gemma":[0.002094097,0.000348095,0.0008473998,0.002101931,0.0006527441,0.0008269784,0.0006683544,0.001107889,0.0005601149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001435076,"about_ca_system_score_gemma":0.001859377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01042441,"about_ca_topic_score_gemma":0.006245771,"domain_scores_codex":[0.9991981,0.0002213167,0.000044144,0.0001688379,0.0003056563,0.00006190012],"domain_scores_gemma":[0.9995377,0.0002074937,0.00003714329,0.00004555142,0.00015295,0.00001912376],"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":[0.00009556787,0.00009703758,0.001070619,0.000145311,0.0001531901,0.0001608328,0.0002164962,0.6618096,0.006730067,0.02090845,0.004026907,0.3045859],"study_design_scores_gemma":[0.00004607107,0.00004474629,0.0003000761,0.00001548868,0.00003068822,0.0001132153,0.00002417895,0.985269,0.002170802,0.008385441,0.00357445,0.00002580265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01078981,0.000636365,0.9856479,0.000131063,0.00005555897,0.0001071906,0.00005806615,0.0007250915,0.001848884],"genre_scores_gemma":[0.1898213,0.0007966912,0.8031501,0.0001041339,0.00006420776,0.0005327803,0.0003809532,0.0001313866,0.005018407],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01042441,"threshold_uncertainty_score":0.02072746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040636211806242,"score_gpt":0.2725208109599084,"score_spread":0.262114448841846,"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."}}