{"id":"W2097507414","doi":"10.1109/icdm.2006.151","title":"Speedup Clustering with Hierarchical Ranking","year":2006,"lang":"en","type":"article","venue":"Proceedings","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada's Michael Smith Genome Sciences Centre","keywords":"Speedup; Cluster analysis; Computer science; Ranking (information retrieval); Pairwise comparison; Data mining; Hierarchical clustering; Algorithm; Artificial intelligence; Parallel computing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002378314,0.000167017,0.0001588931,0.000162872,0.0001918573,0.0003876594,0.0008450542,0.00004892328,0.000007293271],"category_scores_gemma":[0.00002762918,0.000140576,0.00003352646,0.0006186385,0.00008615574,0.0008379868,0.0005189126,0.0003010339,0.00003697044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008623726,"about_ca_system_score_gemma":0.00003673459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003662184,"about_ca_topic_score_gemma":0.000005419473,"domain_scores_codex":[0.9981739,0.000005646571,0.0001827874,0.0005026692,0.0005682766,0.0005667142],"domain_scores_gemma":[0.9994579,0.00003878966,0.00006045197,0.000181328,0.0001589859,0.0001025467],"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":[0.0003667647,0.0005449646,0.02148214,0.000811346,0.0001128308,0.0005405869,0.00448518,0.00785716,0.07469299,0.2837274,0.003119041,0.6022596],"study_design_scores_gemma":[0.002159434,0.0004760812,0.01113912,0.0002778994,0.000009806837,0.0008820939,0.00009921648,0.9206416,0.01952963,0.02583862,0.01788525,0.001061258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05571856,0.0000484017,0.9172934,0.001166157,0.00008837303,0.0002031567,4.30827e-7,0.000541986,0.02493958],"genre_scores_gemma":[0.6723611,0.000002846458,0.3263506,0.00008026473,0.0002316546,0.00002523853,6.20864e-7,0.00002322529,0.0009244451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9127845,"threshold_uncertainty_score":0.5732521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009744494059777837,"score_gpt":0.2418840382192615,"score_spread":0.2321395441594837,"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."}}