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DCg: Interpretable Granulation of Data through GA-based Double Clustering.

, , and . FUZZ-IEEE, page 1-6. IEEE, (2007)

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KERNEL: A Matlab Toolbox for Knowledge Extraction and Refinement by NEural Learning., , and . International Conference on Computational Science (1), volume 2329 of Lecture Notes in Computer Science, page 970-979. Springer, (2002)Incremental indexing of objects in pictorial databases., , and . DMS, page 23-28. Knowledge Systems Institute, (2015)On the Role of Interpretability in Fuzzy Data Mining., , and . International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15 (5): 521-537 (2007)The FISDeT software: Application to beer style classification., , and . FUZZ-IEEE, page 1-6. IEEE, (2017)Feature selection: a neural approach., and . IJCNN, page 3156-3160. IEEE, (1999)Meta-data: Characterization of Input Features for Meta-learning., , and . MDAI, volume 3558 of Lecture Notes in Computer Science, page 457-468. Springer, (2005)A Knowledge-Based Neurocomputing Approach to Extract Refined Linguistic Rules from Data., and . AI*IA, volume 2175 of Lecture Notes in Computer Science, page 40-50. Springer, (2001)An associative memory based on the immune networks., , , and . ICNN, page 519-523. IEEE, (1996)Interpretable Granulation of Medical Data with DC., , and . HIS, page 162-167. IEEE Computer Society, (2007)A Study on Interpretability Conditions for Fuzzy Rule-Based Classifiers., , , and . ISDA, page 438-443. IEEE Computer Society, (2009)