{"id":"W2012552818","doi":"10.5539/cis.v1n4p139","title":"A New Approach for Data Clustering Based on PSO with Local Search","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Particle swarm optimization; Computer science; Context (archaeology); Local optimum; Data mining; Cluster (spacecraft); Mathematical optimization; Swarm behaviour; Local search (optimization); Process (computing); Multi-swarm optimization; TRACE (psycholinguistics); Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008372515,0.001001632,0.001662845,0.001156345,0.00061953,0.001101188,0.001522693,0.001268079,0.001383416],"category_scores_gemma":[0.001908887,0.0005361956,0.001269412,0.00132525,0.0006920996,0.001425276,0.001226668,0.001495632,0.0007507041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005229209,"about_ca_system_score_gemma":0.0006718852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002246848,"about_ca_topic_score_gemma":0.002053049,"domain_scores_codex":[0.9992552,0.0001773063,0.00004607824,0.000171774,0.0003144984,0.00003511691],"domain_scores_gemma":[0.9995306,0.0001582325,0.00005026314,0.00007192498,0.0001591476,0.00002980456],"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":[0.00009921066,0.0001567245,0.001164468,0.0003678443,0.0003909167,0.0002874348,0.0002851882,0.5858769,0.01584623,0.04795193,0.007847659,0.3397254],"study_design_scores_gemma":[0.00002080596,0.00005122364,0.0001585292,0.00001043772,0.00001908936,0.00008017667,0.00001388925,0.9881445,0.001219276,0.005501851,0.004762817,0.00001732914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009175881,0.000110137,0.997802,0.00007093459,0.00005981683,0.00003034129,0.000009436727,0.0002009918,0.0007988061],"genre_scores_gemma":[0.08092579,0.0004713819,0.9138139,0.0002089972,0.0001576329,0.0003654128,0.0001264625,0.0001567163,0.0037736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002246848,"threshold_uncertainty_score":0.004628003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08463517590009528,"score_gpt":0.3103042688114249,"score_spread":0.2256690929113296,"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."}}