{"id":"W2020287868","doi":"10.1016/j.eswa.2007.11.045","title":"A genetic fuzzy <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si218.gif\" overflow=\"scroll\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-Modes algorithm for clustering categorical data","year":2007,"lang":"lv","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Algorithm; Fuzzy logic; Crossover; Categorical variable; Computer science; Genetic algorithm; Operator (biology); Cluster analysis; Mathematics; Artificial intelligence; Machine learning","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":[0.001312681,0.0005209107,0.0003134588,0.0002670316,0.001260533,0.001034924,0.002982226,0.0006432917,0.0002564242],"category_scores_gemma":[0.0001928778,0.0007159947,0.000288761,0.0009701645,0.000523322,0.0009458872,0.002320052,0.0006552302,0.00067312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006366064,"about_ca_system_score_gemma":0.0008229521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00151568,"about_ca_topic_score_gemma":0.0002721815,"domain_scores_codex":[0.993938,0.0001293564,0.001112525,0.001739031,0.001492098,0.001588948],"domain_scores_gemma":[0.9939603,0.0008886759,0.0007240746,0.003504548,0.0002253246,0.0006970828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002305374,0.0003902637,0.000005329672,0.0009708445,0.0005914618,0.0003413811,0.002879855,0.009681406,0.001965685,0.9068717,0.01026526,0.06580625],"study_design_scores_gemma":[0.0009030353,0.0004810371,0.00003036001,0.0003521093,0.0001014071,0.001061666,0.001189944,0.9684522,0.01257798,0.00008170414,0.01407721,0.0006913575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02456071,0.002504152,0.9617415,0.0003319232,0.0008417988,0.0003401249,0.0001965187,0.0002826203,0.00920066],"genre_scores_gemma":[0.802465,0.0003816008,0.1886521,0.0003093408,0.002367247,0.004811464,0.0003333307,0.0003183155,0.0003615327],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9587708,"threshold_uncertainty_score":0.9995291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03440380589158171,"score_gpt":0.2949587150331948,"score_spread":0.2605549091416131,"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."}}