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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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Improving the Classification Ability of DC* Algorithm., , , and . WILF, volume 4578 of Lecture Notes in Computer Science, page 145-151. Springer, (2007)Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks., , , and . IEEE Access, (2020)Crowd Flow Detection from Drones with Fully Convolutional Networks and Clustering., , , , and . IJCNN, page 1-8. IEEE, (2022)Building Cognitive Cities with Explainable Artificial Intelligent Systems., and . CEx@AI*IA, volume 2071 of CEUR Workshop Proceedings, CEUR-WS.org, (2017)Part-Based Data Analysis with Masked Non-negative Matrix Factorization., , and . ICCSA (6), volume 8584 of Lecture Notes in Computer Science, page 440-454. Springer, (2014)Efficiency improvement of DC∗ through a Genetic Guidance., , , and . FUZZ-IEEE, page 1-6. IEEE, (2017)Q-matrix Extraction from Real Response Data Using Nonnegative Matrix Factorizations., , , , and . ICCSA (1), volume 10404 of Lecture Notes in Computer Science, page 203-216. Springer, (2017)Towards fuzzy granulation in OWL ontologies., and . CILC, volume 1459 of CEUR Workshop Proceedings, page 144-158. CEUR-WS.org, (2015)Some Fundamental Interpretability Issues in Fuzzy Modeling., , and . EUSFLAT Conf., page 100-105. Universidad Polytecnica de Catalunya, (2005)Discovering Prediction Rules by a Neuro-fuzzy Modeling Framework., , , and . KES, volume 2773 of Lecture Notes in Computer Science, page 1242-1248. Springer, (2003)