{"id":"W1845902768","doi":"","title":"Revisiting \"Privacy Preserving Clustering by Data Transformation\"","year":2010,"lang":"en","type":"article","venue":"Americanae (AECID Library)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Transformation (genetics); Data mining; Data transformation; Similarity (geometry); Consensus clustering; Information privacy; Fuzzy clustering; CURE data clustering algorithm; Artificial intelligence; Computer security; Image (mathematics); Data warehouse","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01030137,0.001057881,0.0009937627,0.001796073,0.00171159,0.003996495,0.003098325,0.002340595,0.001545888],"category_scores_gemma":[0.01781336,0.0006377985,0.002156038,0.004330568,0.006746213,0.009579316,0.006249536,0.004622371,0.001129384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679832,"about_ca_system_score_gemma":0.002706772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585013,"about_ca_topic_score_gemma":0.001010557,"domain_scores_codex":[0.9842471,0.006873209,0.0007897277,0.002317301,0.005333949,0.0004386125],"domain_scores_gemma":[0.9846686,0.004091899,0.000773129,0.008600926,0.001674615,0.0001910323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009323497,0.0000647246,0.0008393421,0.0003477631,0.000114244,0.0002076004,0.001049491,0.02762532,0.004707077,0.7413116,0.008965683,0.214674],"study_design_scores_gemma":[0.00004028282,0.0001712753,0.0005514256,0.0001757732,0.00006691886,0.00112527,0.0004022512,0.1766974,0.0182746,0.6717701,0.1306318,0.00009281297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002144604,0.0008020631,0.992489,0.001956047,0.0001892832,0.00005365587,0.00007191866,0.0002110473,0.00208236],"genre_scores_gemma":[0.1268074,0.003820629,0.8597076,0.002312416,0.001007699,0.0002562767,0.0003903258,0.0002552708,0.005442291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01030137,"threshold_uncertainty_score":0.05447948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02419756616692578,"score_gpt":0.2624121657609038,"score_spread":0.238214599593978,"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."}}