{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223863,0.0004290872,0.0006065027,0.001519586,0.001139924,0.00144051,0.002129828,0.001320394,0.006803934],"category_scores_gemma":[0.004124444,0.00035092,0.0010874,0.001649818,0.0007008978,0.0007040647,0.0008425842,0.001157946,0.002395255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912308,"about_ca_system_score_gemma":0.002484758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02485346,"about_ca_topic_score_gemma":0.02555266,"domain_scores_codex":[0.9990734,0.0001482301,0.00004404081,0.0002589134,0.0004216585,0.0000537694],"domain_scores_gemma":[0.9991124,0.0002662407,0.00004191049,0.0001384516,0.0003944465,0.00004645674],"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":[0.000205394,0.0001891132,0.002304127,0.0001863163,0.0001334223,0.0001714688,0.0003514629,0.216504,0.0158596,0.08961814,0.01764373,0.6568332],"study_design_scores_gemma":[0.00004292267,0.00007471246,0.0006493525,0.00004807624,0.0000525729,0.0001478668,0.0000673916,0.9505681,0.007548164,0.02914919,0.01161047,0.00004110689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008415041,0.00007960118,0.9840109,0.0002324942,0.00007005004,0.00009829771,0.0003482506,0.0008099753,0.005935405],"genre_scores_gemma":[0.0777313,0.000104123,0.9109325,0.0001855394,0.00004074275,0.0001903005,0.0007285099,0.0001582689,0.009928742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02485346,"threshold_uncertainty_score":0.04941761,"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."}}