{"id":"W1992565631","doi":"10.1109/dbkda.2010.32","title":"Clustering Relational Database Entities Using K-means","year":2010,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Cluster analysis; Homogeneous; Data type; Task (project management); Perspective (graphical); Relational database; Vectorization (mathematics); Data mining; Process (computing); The Internet; Information retrieval; Database; Parallel computing; Artificial intelligence; World Wide Web; Programming language; 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":[0.001693515,0.001145645,0.002269789,0.006383135,0.001851723,0.004244387,0.003296955,0.001547073,0.002471976],"category_scores_gemma":[0.008011459,0.0008842284,0.002115392,0.009407375,0.0006380037,0.00328681,0.001827469,0.001131637,0.002322136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001203659,"about_ca_system_score_gemma":0.001931188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01533127,"about_ca_topic_score_gemma":0.01647856,"domain_scores_codex":[0.9956368,0.00069879,0.0006314496,0.001284237,0.001514439,0.0002343108],"domain_scores_gemma":[0.996841,0.0008903982,0.000280746,0.000836944,0.001074476,0.00007636763],"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":[0.0003727807,0.0002960152,0.006399703,0.0005281979,0.0004857597,0.0001849805,0.0009884933,0.1582645,0.009419716,0.01028962,0.0104799,0.8022903],"study_design_scores_gemma":[0.00003520984,0.00006512392,0.002305677,0.00004734772,0.0001155637,0.0002109681,0.0005829853,0.956175,0.01094927,0.01632482,0.01310556,0.0000826077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01770034,0.000486566,0.9770183,0.000156955,0.00004332036,0.0002573454,0.000899396,0.002408954,0.00102877],"genre_scores_gemma":[0.1023613,0.0003673317,0.8923753,0.00005048097,0.00003100805,0.0002528407,0.003215628,0.0001494992,0.001196567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01533127,"threshold_uncertainty_score":0.03048408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04904588250724925,"score_gpt":0.2780259401993471,"score_spread":0.2289800576920978,"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."}}