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dc.contributor.authorFernández, Alberto .
dc.contributor.authorCarmona del Jesús, Cristóbal José 
dc.contributor.authorJesus, María José del .
dc.contributor.authorHerrera, Francisco .
dc.date.accessioned2018-05-18T07:29:26Z
dc.date.available2018-05-18T07:29:26Z
dc.date.issued2016-04
dc.identifier.issn1875-6891
dc.identifier.urihttp://hdl.handle.net/10259/4794
dc.description.abstractCurrently, 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 topicen
dc.description.sponsorshipSpanish Ministry of Science and Technology under project TIN2014-57251-P; the Andalusian Research Plan P11-TIC-7765; and both the University of Ja´en and Caja Rural Provincial de Ja´en under project UJA2014/06/15.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherAtlantis Pressen
dc.relation.ispartofPublication Cover International Journal of Computational Intelligence Systems. 2016, V. 9, supl. 1, p. 69-80en
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectBig Dataen
dc.subjectFuzzy Rule Based Classification Systems,en
dc.subjectClusteringen
dc.subjectMapReduceen
dc.subjectHadoopen
dc.subjectSparken
dc.subjectFlinken
dc.titleA view on Fuzzy Systems for big data: progress and opportunitiesen
dc.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.1080/18756891.2016.1180820
dc.identifier.doi10.1080/18756891.2016.1180820
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINCYT/TIN2014-57251-P
dc.relation.projectIDinfo:eu-repo/grantAgreement/JA/P11-TIC-7765
dc.relation.projectIDinfo:eu-repo/grantAgreement/UJA/UJA2014/06/15
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen


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