{"id":"W4306167002","doi":"10.5194/isprs-annals-x-4-w3-2022-81-2022","title":"DISCOVERING CLUSTERING PATTERNS FROM E-COUNTER DATA STREAMS","year":2022,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Data stream mining; Cluster analysis; Computer science; Data mining; Data stream; STREAMS; Affinity propagation; Data stream clustering; Machine learning; CURE data clustering algorithm; Computer network; Correlation clustering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001086733,0.0007904646,0.0005525305,0.003196908,0.0003622494,0.001117467,0.0008867549,0.000665407,0.0004558746],"category_scores_gemma":[0.004234502,0.0001965039,0.0004253884,0.002907723,0.0003578678,0.0009671182,0.0006816579,0.0006374873,0.0004119829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000654096,"about_ca_system_score_gemma":0.0005938244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003502164,"about_ca_topic_score_gemma":0.004414841,"domain_scores_codex":[0.9991946,0.0001395098,0.00007492332,0.000206983,0.0002914997,0.00009255952],"domain_scores_gemma":[0.9967084,0.001367197,0.0004023902,0.0003241523,0.001080207,0.0001175835],"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":[0.0008603259,0.0008783889,0.1446162,0.0003606497,0.000228558,0.0009903613,0.000603809,0.468518,0.02178468,0.005846833,0.004767266,0.3505449],"study_design_scores_gemma":[0.000009725371,0.0000598818,0.01008967,0.00001259273,0.00001261207,0.0001070172,0.0001801679,0.9813141,0.004977648,0.002190825,0.001032507,0.00001318787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4961655,0.0002974253,0.496401,0.0004190153,0.0001062183,0.0003584925,0.002983083,0.00160305,0.001666317],"genre_scores_gemma":[0.8686846,0.0001694416,0.1261116,0.00004735651,0.00004205172,0.0001638528,0.003920329,0.00003918493,0.0008216487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003502164,"threshold_uncertainty_score":0.006963551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04889879753834387,"score_gpt":0.277419060596125,"score_spread":0.2285202630577812,"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."}}