{"id":"W4312723533","doi":"10.1109/access.2022.3228238","title":"Piecemeal Clustering: a Self-Driven Data Clustering Algorithm","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Equinor","keywords":"Cluster analysis; CURE data clustering algorithm; Computer science; Canopy clustering algorithm; Correlation clustering; Data stream clustering; Fuzzy clustering; Data mining; Determining the number of clusters in a data set; Single-linkage clustering; Clustering high-dimensional data; Algorithm; Constrained clustering; Data set; Affinity propagation; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002412549,0.001460916,0.001854131,0.004359717,0.002394049,0.001814806,0.004989503,0.002829771,0.002979344],"category_scores_gemma":[0.007623624,0.0008108875,0.001823396,0.004550786,0.001489165,0.002968573,0.003504016,0.002098008,0.002346369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00124073,"about_ca_system_score_gemma":0.001791451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005333617,"about_ca_topic_score_gemma":0.00749473,"domain_scores_codex":[0.9970709,0.0005730172,0.000142101,0.0008293331,0.001231242,0.0001532738],"domain_scores_gemma":[0.9972658,0.0005946855,0.0002820248,0.0006936789,0.001046009,0.0001177941],"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":[0.0004076158,0.0002000354,0.002919369,0.0003647863,0.0004143955,0.0001701875,0.0008821908,0.1878708,0.01716602,0.02004012,0.0199085,0.749656],"study_design_scores_gemma":[0.00004157165,0.00008847035,0.000792915,0.00003591059,0.00003912311,0.0002723455,0.0001410665,0.956903,0.007479373,0.01611858,0.0180004,0.00008725106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004733793,0.00022628,0.9922982,0.0001373448,0.0000766673,0.0001420328,0.0001079433,0.001439418,0.0008382846],"genre_scores_gemma":[0.05320793,0.0001653626,0.9427001,0.000199704,0.0000746245,0.0002919124,0.0006866773,0.0004160633,0.002257695],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005333617,"threshold_uncertainty_score":0.01275891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09243514291304206,"score_gpt":0.3723603590328441,"score_spread":0.279925216119802,"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."}}