{"id":"W1988873435","doi":"10.1109/iv.2012.51","title":"Using Clustering to Personalize Visualization","year":2012,"lang":"en","type":"article","venue":"2012 16th International Conference on Information Visualisation","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Visualization; Computer science; Cluster analysis; Information visualization; Set (abstract data type); Data visualization; Data mining; Human–computer interaction; Machine learning","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006350949,0.0002194165,0.0001555546,0.0006794722,0.0001963008,0.0007148939,0.0006774365,0.00009669286,0.0007102939],"category_scores_gemma":[0.0001969114,0.0002263225,0.00006198086,0.0005481048,0.00002354443,0.01072769,0.0002157113,0.0001033551,0.001143055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002724659,"about_ca_system_score_gemma":0.00008812606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002393716,"about_ca_topic_score_gemma":0.000003748474,"domain_scores_codex":[0.9978836,0.00009353164,0.000592184,0.0002033272,0.0008748921,0.0003524425],"domain_scores_gemma":[0.9984226,0.00003646732,0.0003397981,0.0003239893,0.000617106,0.0002600457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001743982,0.00006317061,0.0005593882,0.00001454914,0.00002265087,1.676327e-7,0.003873679,0.001503681,0.0003834015,0.9826047,0.002607579,0.008349574],"study_design_scores_gemma":[0.0004279279,0.00006618194,0.001798127,0.00007071456,0.000009429179,0.00001199219,0.0005895442,0.9406692,0.0008825051,0.0006532772,0.0544383,0.0003827792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00285607,0.000005993488,0.9755217,0.0008466892,0.001681483,0.0002553932,0.00003786916,0.0002168257,0.01857796],"genre_scores_gemma":[0.9700588,0.00002206908,0.02329076,0.005176315,0.0003855929,0.00003196143,0.0005740862,0.00001627948,0.0004441288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9819514,"threshold_uncertainty_score":0.9996347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306298615664563,"score_gpt":0.3910976901110261,"score_spread":0.2604678285445698,"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."}}