{"id":"W2106899369","doi":"10.1142/s0218001408006922","title":"CLUSTERING QUALITY MEASURES BASED ON COMPARING THE PROXIMITY MATRICES FOR THE MEMBERSHIP VECTORS AND THE OBJECTS","year":2008,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Cluster analysis; Data mining; Pattern recognition (psychology); Rand index; Partition (number theory); Mathematics; Feature vector; Artificial intelligence; Fuzzy clustering; Correlation clustering; Metric (unit); Feature (linguistics); Single-linkage clustering; Computer science; CURE data clustering algorithm; Combinatorics","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":[],"consensus_categories":[],"category_scores_codex":[0.001882533,0.0001103332,0.000146628,0.00009839332,0.0004106798,0.0003096133,0.0007951935,0.00002825791,0.000007544607],"category_scores_gemma":[0.0006212842,0.00005618709,0.00009062941,0.0001151615,0.0003181825,0.000371182,0.0001433356,0.0002906227,0.00000358641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004030726,"about_ca_system_score_gemma":0.00004518276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007577491,"about_ca_topic_score_gemma":0.0000784145,"domain_scores_codex":[0.998331,0.0002592756,0.0004443524,0.0001719737,0.0006387201,0.000154667],"domain_scores_gemma":[0.9963726,0.002582567,0.0003226917,0.00014875,0.0005202855,0.00005310805],"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":[0.0006570618,0.00007882986,0.0007369697,0.00002232552,0.0000976222,0.00001939736,0.003445579,0.007809798,0.0001453048,0.000732835,0.00001520672,0.9862391],"study_design_scores_gemma":[0.0003791025,0.0001257429,0.002251041,0.0001426477,0.00001447994,0.0002029531,0.0007906226,0.9766102,0.005966608,0.01328852,0.00009514498,0.0001329094],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05405933,0.0001490708,0.9381167,0.006735107,0.0006201108,0.0002489363,0.00000505502,0.00001328292,0.00005242268],"genre_scores_gemma":[0.9962305,0.0001921558,0.002462429,0.0007946873,0.0002858584,0.00002061077,8.190734e-7,0.00000603293,0.000006852109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9861062,"threshold_uncertainty_score":0.3158658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2650123592300616,"score_gpt":0.3802203558425662,"score_spread":0.1152079966125046,"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."}}