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oai:riubu.ubu.es:10259/47942021-11-10T09:38:23Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Fernández, Alberto . Carmona del Jesús, Cristóbal José Jesus, María José del . Herrera, Francisco . Big Data Fuzzy Rule Based Classification Systems, Clustering MapReduce Hadoop Spark Flink Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. By means of the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Data environment in the different learning areas is at a preliminary stage yet. In this paper, we will carry out an overview of the main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss those problems related to the data distribution and parallelization of the current algorithms, and also its relationship with the fuzzy representation of the information. Finally, we will provide our view on the expectations for the future in this framework according to the design of those methods based on fuzzy sets, as well as the open challenges on the topic 2018-05-18 2018-05-18 2016-04 info:eu-repo/semantics/article 1875-6891 http://hdl.handle.net/10259/4794 10.1080/18756891.2016.1180820 eng Publication Cover International Journal of Computational Intelligence Systems. 2016, V. 9, supl. 1, p. 69-80 https://doi.org/10.1080/18756891.2016.1180820 info:eu-repo/grantAgreement/MINCYT/TIN2014-57251-P info:eu-repo/grantAgreement/JA/P11-TIC-7765 info:eu-repo/grantAgreement/UJA/UJA2014/06/15 http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial 4.0 International Atlantis Press