{"id":"W129927049","doi":"10.1007/978-3-642-20841-6_17","title":"Spatial Entropy-Based Clustering for Mining Data with Spatial Correlation","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Spatial analysis; Cluster analysis; Spatial correlation; Computer science; Data mining; Entropy (arrow of time); Similarity (geometry); Spatial dependence; Pattern recognition (psychology); Spatial relation; Correlation; Spatial database; Artificial intelligence; Mathematics; Statistics","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0009571521,0.0005841468,0.0005385902,0.0007710923,0.0003767873,0.0005332042,0.00584112,0.0002850451,0.00001770224],"category_scores_gemma":[0.0001879285,0.0005243631,0.00007370915,0.0003955794,0.0006138082,0.0009863043,0.003142991,0.0006999583,0.00001402474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003636481,"about_ca_system_score_gemma":0.00100411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001702243,"about_ca_topic_score_gemma":0.0008332422,"domain_scores_codex":[0.9949025,0.00003992947,0.0005341734,0.002322224,0.001260606,0.0009405772],"domain_scores_gemma":[0.9953642,0.0007982156,0.0003966287,0.002838435,0.0003844787,0.0002180389],"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":[0.0000651276,0.00002122481,0.00004705937,0.00006377127,0.00001168526,0.0000559725,0.0003211203,0.1925691,0.00004072905,0.0004277417,0.000005124939,0.8063713],"study_design_scores_gemma":[0.0007822499,0.0005384857,0.00007147305,0.0004788101,0.00001126958,0.00004462683,1.390463e-7,0.9897507,0.0004808616,0.006392714,0.0008163249,0.0006323489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001445592,0.00007438807,0.9966699,0.0002549827,0.001513038,0.000901768,0.00003502686,0.0001937535,0.0003426143],"genre_scores_gemma":[0.05642306,0.000005715434,0.9423057,0.0002820291,0.0006526429,0.00002982054,0.00006863422,0.00006910368,0.0001632909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.805739,"threshold_uncertainty_score":0.9997208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04558991400462283,"score_gpt":0.2901130431364683,"score_spread":0.2445231291318455,"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."}}