{"id":"W2012960609","doi":"10.1109/icmla.2014.25","title":"Semi-supervised Kernel-Based Temporal Clustering","year":2014,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence; Spectral clustering; Kernel (algebra); Pattern recognition (psychology); Correlation clustering; Constrained clustering; Canopy clustering algorithm; Data mining; CURE data clustering algorithm; Fuzzy clustering; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002933181,0.0001088416,0.0001512673,0.00006413394,0.0001199204,0.0002004777,0.000547983,0.00003556043,0.0001618281],"category_scores_gemma":[0.00002537336,0.00008822514,0.0000971505,0.0002758675,0.00002011912,0.0002656689,0.0001913242,0.00006383166,0.00009858388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001446517,"about_ca_system_score_gemma":0.00001950179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000105719,"about_ca_topic_score_gemma":0.00006147139,"domain_scores_codex":[0.9990718,0.00003730341,0.0001937596,0.000287526,0.0001704821,0.0002391681],"domain_scores_gemma":[0.9992971,0.00004687499,0.00005242546,0.0004678,0.00004387524,0.0000918752],"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":[0.00003521498,0.0002623613,0.04475271,0.0001556909,0.000134941,0.0000340782,0.001231589,0.07138553,0.006942032,0.09575927,0.006642422,0.7726641],"study_design_scores_gemma":[0.0001892928,0.00004340696,0.0006187562,0.000009176371,0.0000035376,0.000002143499,0.00001328523,0.9891415,0.0006831469,0.0002757333,0.008885464,0.0001345203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008738654,0.00001193414,0.9626036,0.0005962176,0.0001081858,0.00003744366,2.249775e-7,0.0002253241,0.02767844],"genre_scores_gemma":[0.9078316,3.353281e-7,0.09016448,0.0005665053,0.00007455704,0.000002838768,0.000002290829,0.000007115783,0.001350262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.917756,"threshold_uncertainty_score":0.3597717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475038148313849,"score_gpt":0.2136434464422008,"score_spread":0.1988930649590623,"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."}}