Martos, L. A. P., García-Vico, Á. M., González, P., & Carmona, C. J. (2023). A Multiclustering Evolutionary Hyperrectangle-Based Algorithm. International Journal of Computational Intelligence Systems, 16. https://doi.org/10.1007/S44196-023-00341-3
data mining
Pérez Martos, L. A., García-Vico, Á. M., González, P., & Carmona, C. J. (2023). An Evolutionary Fuzzy System for Multiclustering in Data Streaming. 230, 33-43. Elsevier B.V. https://doi.org/10.1016/J.PROCS.2023.12.058
Fernández, A., López, V., del Jesus Díaz, M. J., & Herrera Triguero, F. (2015). Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems, 80, 109-121. https://doi.org/10.1016/j.knosys.2015.01.013
Palacios, A. M., Palacios, J. L., Sánchez, L., & Alcala-Fdez, J. (2015). Genetic learning of the membership functions for mining fuzzy association rules from low quality data. Information Sciences, 295, 358-378. https://doi.org/10.1016/j.ins.2014.10.027
Luna, J. M., Pechenizkiy, M., del Jesus Díaz, M. J., & Ventura, S. (2018). Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming. IEEE Transactions on Cybernetics, 48, 3030-3044. https://doi.org/10.1109/TCYB.2017.2750919 (Original work published 2026)
Sánchez, O., Moyano, J. M., Sánchez, L., & Alcala-Fdez, J. (2017). Mining association rules in R using the package RKEEL. 1-6. https://doi.org/10.1109/FUZZ-IEEE.2017.8015572 (Original work published 2026)
Segura-Delgado, A., Gacto, M. J., Alcalá, R., & Alcala-Fdez, J. (2020). Temporal association rule mining: An overview considering the time variable as an integral or implied component. WIREs Data Mining and Knowledge Discovery, 10. https://doi.org/10.1002/widm.1367 (Original work published 2020)
Gacto, M. J., Soto-Hidalgo, J. M., Alcala-Fdez, J., & Alcalá, R. (2019). Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems. IEEE Access, 7, 108916-108939. https://doi.org/10.1109/ACCESS.2019.2933261 (Original work published 2019)
Palacios, A. M., Sánchez, L., & Couso, I. (2013). CI-LQD: A software tool for modeling and decision making with Low Quality Data. 1-8. https://doi.org/10.1109/FUZZ-IEEE.2013.6622418 (Original work published 2026)
Pérez, P., Frías, M. P., Pérez Godoy, M. D., Rivera Rivas, A. J., Jesus, M. J. d., Parras, M., & Torres, F. J. (2008). An Study on Data Mining Methods for Short-Term Forecasting of the Extra Virgin Olive Oil Price in the Spanish Market. 943-946. https://doi.org/10.1109/HIS.2008.132 (Original work published 2026)
Pagination
- Page 1
- Next page