{"id":"W1844869736","doi":"10.1007/978-3-7908-1825-3_2","title":"Granular Computing in Data Mining","year":2001,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Data mining; Cardinality (data modeling); Granular computing; Computer science; Consistency (knowledge bases); Interpretability; Flexibility (engineering); Relevance (law); Granulation; Artificial intelligence; Rough set; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005021517,0.001214085,0.002267475,0.004069287,0.0009448059,0.006776699,0.001927739,0.002165286,0.003987213],"category_scores_gemma":[0.01126015,0.0006547977,0.00112679,0.007619904,0.004164112,0.006574345,0.003030292,0.003115907,0.001366593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00215442,"about_ca_system_score_gemma":0.001407187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659513,"about_ca_topic_score_gemma":0.001157943,"domain_scores_codex":[0.9959087,0.001591979,0.0004123445,0.0005931919,0.001352058,0.000141723],"domain_scores_gemma":[0.995396,0.003381413,0.0002171914,0.0006226441,0.0002782412,0.0001045462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005802099,0.00003677344,0.0004858228,0.001093697,0.00009624012,0.0002535872,0.0004087715,0.02103612,0.0004167241,0.8236732,0.01192341,0.1405175],"study_design_scores_gemma":[0.00002258987,0.00003380736,0.0002924995,0.0003855616,0.00003260326,0.0002040533,0.0001416951,0.0363756,0.0003406271,0.8903506,0.07178374,0.0000366096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004693652,0.09400296,0.8367065,0.007750677,0.001808011,0.000380225,0.0005196598,0.0008178359,0.05332041],"genre_scores_gemma":[0.1750499,0.07542387,0.7252694,0.003115426,0.004021822,0.001123463,0.001039841,0.0002638598,0.01469236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006776699,"threshold_uncertainty_score":0.02655661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1293684798913958,"score_gpt":0.3291165410606664,"score_spread":0.1997480611692706,"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."}}