{"id":"W4389326698","doi":"10.1016/j.asoc.2023.111131","title":"Three-way clustering: Foundations, survey and challenges","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Consensus clustering; GRASP; Fuzzy clustering; Data mining; Constrained clustering; Correlation clustering; Cluster (spacecraft); Set (abstract data type); Conceptual clustering; Artificial intelligence; CURE data clustering algorithm","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":[0.0009841485,0.0001417957,0.0001723314,0.00009929207,0.0003452038,0.000232654,0.0004859659,0.00005894443,0.00000189495],"category_scores_gemma":[0.00003971846,0.0001341398,0.00002430758,0.0004298301,0.00004310231,0.0001025073,0.0008346959,0.0001179175,0.0001265341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001471905,"about_ca_system_score_gemma":0.00001997072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004114891,"about_ca_topic_score_gemma":0.0001300987,"domain_scores_codex":[0.9987624,0.00003427701,0.000204558,0.0004654767,0.0001847406,0.0003485128],"domain_scores_gemma":[0.9989091,0.0005256976,0.00007516926,0.0003807792,0.00003243001,0.00007679263],"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":[0.000003364157,0.00002098177,0.001894351,0.00004766261,0.00002080838,0.000008693215,0.001275921,0.001564492,0.00003632011,0.07858235,0.0005690306,0.915976],"study_design_scores_gemma":[0.0003026177,0.00003178208,0.2631599,0.00001445578,0.000002463191,0.000009677868,0.00009142952,0.716153,0.000006937684,0.01650937,0.003418563,0.0002998127],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02626805,0.0005354348,0.9635923,0.001330183,0.0004171094,0.0002224451,0.000001602283,0.000983733,0.006649132],"genre_scores_gemma":[0.9601537,0.0001694132,0.0393558,0.000185993,0.00009408464,0.000006493735,0.00001183343,0.00001369117,0.000009023142],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9338856,"threshold_uncertainty_score":0.5470061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09446223775692422,"score_gpt":0.2758367361530555,"score_spread":0.1813744983961313,"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."}}