{"id":"W2129500661","doi":"10.1109/tkde.2008.162","title":"Mining Projected Clusters in High-Dimensional Spaces","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cluster analysis; Computer science; Curse of dimensionality; Linear subspace; Outlier; Clustering high-dimensional data; Data mining; CURE data clustering algorithm; Data point; Correlation clustering; Computation; Pattern recognition (psychology); Canopy clustering algorithm; Single-linkage clustering; Algorithm; Artificial intelligence; Mathematics","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":[0.003843067,0.001458915,0.002320229,0.005876507,0.002084518,0.003404848,0.002761962,0.002166633,0.0009169714],"category_scores_gemma":[0.01857025,0.001056979,0.001927939,0.005983123,0.001610611,0.003401351,0.00403104,0.001920173,0.00085037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009341603,"about_ca_system_score_gemma":0.001622829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003376128,"about_ca_topic_score_gemma":0.003143603,"domain_scores_codex":[0.9943976,0.001717083,0.0003905838,0.001161525,0.001996562,0.0003366333],"domain_scores_gemma":[0.9909,0.003748921,0.0009833293,0.001255505,0.002773721,0.0003384296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007366231,0.0005352815,0.02626669,0.0008861371,0.000890365,0.001501383,0.002813942,0.515168,0.009665623,0.06391372,0.01102143,0.3666008],"study_design_scores_gemma":[0.00003637567,0.00007805529,0.002673762,0.00004689402,0.00004192767,0.0002743098,0.0006276823,0.919227,0.002476842,0.0724598,0.002007036,0.00005036467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06225463,0.0003778762,0.9349715,0.000251377,0.00004252632,0.0001841339,0.0004301528,0.0006967588,0.0007910997],"genre_scores_gemma":[0.3365365,0.0005125635,0.6580942,0.0001014015,0.00009495959,0.0004147555,0.002869759,0.0001330442,0.001242849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005876507,"threshold_uncertainty_score":0.02032435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03923416376241975,"score_gpt":0.2902071390881266,"score_spread":0.2509729753257069,"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."}}