{"id":"W1510884249","doi":"10.1137/1.9781611972757.14","title":"A Cutting Algorithm for the Minimum Sum-of-Squared Error Clustering","year":2005,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Cluster analysis; Computer science; Algorithm; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.00008417752,0.00009548829,0.0001148061,0.00003749772,0.00005069322,0.00001849139,0.0001369961,0.00004240552,0.0000198158],"category_scores_gemma":[0.00001189633,0.00007033846,0.00006927393,0.00006236566,0.00001854934,0.00005809159,0.00003321893,0.00005989734,0.000003597467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001692992,"about_ca_system_score_gemma":0.000004629245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001745492,"about_ca_topic_score_gemma":0.00002377668,"domain_scores_codex":[0.9994988,0.000005532702,0.0001680572,0.00008688917,0.00006866531,0.0001720036],"domain_scores_gemma":[0.9995813,0.0001400821,0.00002381544,0.0001961424,0.00003774322,0.0000208984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005626189,0.0000151363,0.00001419888,0.00002329499,0.00007412948,0.000001102547,0.0004395188,0.02371112,0.01577001,0.0001392177,0.009899219,0.9499074],"study_design_scores_gemma":[0.0001260942,0.00001628218,0.00002594457,0.00003081176,0.00001369602,0.000004338878,0.000129537,0.9004624,0.0841236,0.00006367703,0.01491454,0.00008911594],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008480945,0.0003953837,0.986101,0.0001733038,0.0001741996,0.0002607859,0.000004364487,0.000647191,0.003762778],"genre_scores_gemma":[0.7686248,0.000018843,0.2308338,0.00007403958,0.0001967952,0.00002433601,0.000001521297,0.00002716615,0.0001987217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9498183,"threshold_uncertainty_score":0.2868319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613100460965655,"score_gpt":0.2633180802274417,"score_spread":0.2371870756177852,"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."}}