{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002849197,0.001874124,0.001614254,0.002855173,0.0007928715,0.003977057,0.00182447,0.001463789,0.002707739],"category_scores_gemma":[0.01340986,0.001210804,0.001810937,0.001707025,0.0008849183,0.004716999,0.002317096,0.002419059,0.001428738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272986,"about_ca_system_score_gemma":0.0009690468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005061213,"about_ca_topic_score_gemma":0.005440877,"domain_scores_codex":[0.9977356,0.0006298615,0.0001325837,0.0008312781,0.0005137863,0.0001568054],"domain_scores_gemma":[0.9921068,0.002503382,0.0006607173,0.002789922,0.001605335,0.0003338412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008855502,0.0004032774,0.01521675,0.0005683171,0.0005200013,0.0002511952,0.002455397,0.3109347,0.03113467,0.03129474,0.02624278,0.5800927],"study_design_scores_gemma":[0.0000420896,0.0000634026,0.002104125,0.00006916482,0.00008187631,0.0001486171,0.0002496611,0.9360581,0.01261576,0.03318648,0.01526416,0.0001166807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01132813,0.0002261188,0.9778001,0.0003297876,0.00006822967,0.0001122839,0.0002832457,0.008621882,0.001230214],"genre_scores_gemma":[0.2236074,0.0006418129,0.7697163,0.0001911811,0.0001281719,0.0003166064,0.001134276,0.001931382,0.00233282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005061213,"threshold_uncertainty_score":0.01506817,"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."}}