{"id":"W2357436809","doi":"","title":"Data Stream Clustering Algorithm Based on the Frequent Pattern","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tree traversal; Computer science; Cluster analysis; Tree (set theory); Data stream; Data stream clustering; Data mining; Data stream mining; Algorithm; Pattern recognition (psychology); Artificial intelligence; CURE data clustering algorithm; Correlation clustering; 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":[0.0008447414,0.0008053631,0.001422239,0.0033849,0.001182226,0.001247376,0.001665212,0.0007467399,0.001651999],"category_scores_gemma":[0.003076794,0.0003845409,0.000889731,0.004058767,0.000380884,0.00272023,0.0008741873,0.001109074,0.00107173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007997707,"about_ca_system_score_gemma":0.001554198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003940541,"about_ca_topic_score_gemma":0.002928355,"domain_scores_codex":[0.9989129,0.0001170763,0.0001063022,0.0002782599,0.0004995249,0.0000858261],"domain_scores_gemma":[0.9989302,0.0001750873,0.00008900526,0.0001113782,0.000636782,0.00005773451],"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":[0.0004273953,0.0002085274,0.004663337,0.0004217566,0.0002360733,0.0002692973,0.000294016,0.07024355,0.01258297,0.02589104,0.02211052,0.8626516],"study_design_scores_gemma":[0.0001067162,0.0001437902,0.001656969,0.00004195263,0.000071053,0.0006398344,0.0001316755,0.9421509,0.01251529,0.02809029,0.01438609,0.00006543774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01265673,0.000508369,0.9828191,0.0002494416,0.0001872555,0.0001920237,0.0004058809,0.001446845,0.001534431],"genre_scores_gemma":[0.1283782,0.0009785476,0.8638697,0.0001253227,0.0002032289,0.0003964652,0.002224465,0.0001665463,0.003657454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003940541,"threshold_uncertainty_score":0.007835209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02803405132143296,"score_gpt":0.2384274037799198,"score_spread":0.2103933524584868,"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."}}