{"id":"W1875624538","doi":"10.1007/s11634-015-0219-5","title":"Factor probabilistic distance clustering (FPDC): a new clustering method","year":2015,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Università degli Studi di Napoli Federico II","keywords":"Cluster analysis; Correlation clustering; CURE data clustering algorithm; Single-linkage clustering; Probabilistic logic; Canopy clustering algorithm; Computer science; Data stream clustering; Fuzzy clustering; Determining the number of clusters in a data set; Clustering high-dimensional data; k-medians clustering; Transformation (genetics); Mathematics; Pattern recognition (psychology); Data mining; Artificial intelligence","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":[0.0007759865,0.0001954136,0.0003560179,0.0003590242,0.00009069555,0.0002865333,0.001693646,0.00006042879,0.000005051982],"category_scores_gemma":[0.0004747242,0.0001830607,0.00004044593,0.002096097,0.0000646219,0.003023096,0.001310367,0.000200985,0.000008550034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001890646,"about_ca_system_score_gemma":0.0001024315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001056882,"about_ca_topic_score_gemma":0.002681303,"domain_scores_codex":[0.9974205,0.0001697138,0.0004622464,0.001104623,0.0004795098,0.0003633558],"domain_scores_gemma":[0.9972367,0.0002181499,0.0001983853,0.001992141,0.0001043037,0.000250365],"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":[0.00002878092,0.00005604065,0.003260982,0.00006133363,0.00007632573,0.000009487178,0.0006257115,0.04435191,0.000288549,0.002208906,0.00004463019,0.9489874],"study_design_scores_gemma":[0.0003160083,0.00002549439,0.006204216,0.00002629951,0.00003763982,0.000005523121,0.0001393519,0.979554,0.00002533683,0.002718244,0.01073259,0.0002152854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002538057,0.001424383,0.9970609,0.0005715584,0.0001093912,0.0001914346,0.00003066456,0.00008627451,0.0002716128],"genre_scores_gemma":[0.2334704,0.0008756719,0.7649428,0.00004584884,0.0000817183,0.00003852721,0.0001452976,0.00001666125,0.0003831265],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9487721,"threshold_uncertainty_score":0.7464998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1223525331876487,"score_gpt":0.4083881885969034,"score_spread":0.2860356554092547,"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."}}