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dc.contributor.authorLópez Belmonte, Jesús 
dc.contributor.authorSegura Robles, Adrián 
dc.contributor.authorMoreno Guerrero, Antonio José 
dc.contributor.authorParra González, María Elena 
dc.date.accessioned2020-07-06T09:41:09Z
dc.date.available2020-07-06T09:41:09Z
dc.date.issued2020-03
dc.identifier.citationLópez Belmonte, J., Segura-Robles, A., Moreno-Guerrero, A. J., & Parra-González, M. E. (2020). Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science. Symmetry, 12(4), 495. [doi:10.3390/sym12040495]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/62877
dc.description.abstractCombined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectScientific productiones_ES
dc.subjectBibliometric analysises_ES
dc.subjectMachine learninges_ES
dc.subjectBig Dataes_ES
dc.subjectWeb of Sciencees_ES
dc.titleMachine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Sciencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/sym12040495


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Atribución 3.0 España
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