{"id":"W23725892","doi":"10.1007/978-3-642-37186-8_4","title":"Spectral Clustering: An Explorative Study of Proximity Measures","year":2013,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cluster analysis; Spectral clustering; Measure (data warehouse); Computer science; Euclidean distance; Similarity (geometry); Data mining; Similarity measure; Distance measures; Distance matrix; Usability; Boundary (topology); Artificial intelligence; Pattern recognition (psychology); Mathematics; Algorithm","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":[0.002135654,0.000774844,0.0008839648,0.004135643,0.001159052,0.004420644,0.0017911,0.0009907235,0.002793835],"category_scores_gemma":[0.01453714,0.0005482521,0.0008092217,0.009239113,0.002298512,0.006515014,0.002363623,0.00148498,0.0005577549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175838,"about_ca_system_score_gemma":0.0008019399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226367,"about_ca_topic_score_gemma":0.001256901,"domain_scores_codex":[0.9981691,0.0007754226,0.00007197526,0.0003473894,0.0005719544,0.00006408668],"domain_scores_gemma":[0.992721,0.005596976,0.0003799453,0.0006790533,0.0004796114,0.0001433375],"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":[0.0000528982,0.00006220752,0.003319941,0.0006397779,0.0001180804,0.0001374044,0.00297176,0.02205807,0.002420671,0.8242055,0.00519826,0.1388155],"study_design_scores_gemma":[0.000009377695,0.00005248261,0.002385895,0.0001575873,0.00005467372,0.0005393065,0.00162972,0.1196336,0.001745022,0.8503008,0.02344264,0.00004870328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06928739,0.01050438,0.895322,0.001256468,0.0001455176,0.00009426373,0.0004468671,0.0003172761,0.02262583],"genre_scores_gemma":[0.504238,0.008972731,0.4787388,0.0002388166,0.0003882041,0.0001945664,0.0007385333,0.0002874682,0.006202966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004420644,"threshold_uncertainty_score":0.01129454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191461492858851,"score_gpt":0.3554448074291799,"score_spread":0.2362986581432948,"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."}}