Rough Sets: A Bibliometric Analysis from 2014 to 2018 Heradio, Ruben Fernandez-Amoros, David Moral-Muñoz, Jose A. Cobo, Manuel J. 2020-01-04T07:30:36Z 2020-01-04T07:30:36Z 2020-01-07
dc.description.abstract Along almost forty years, considerable research has been undertaken on rough set theory to deal with vague information. Rough sets have proven to be extremely helpful for a diversity of computer-science problems (e.g., knowledge discovery, computational logic, machine learning, etc.), and numerous application domains (e.g., business economics, telecommunications, neurosciences, etc.). Accordingly, the literature on rough sets has grown without ceasing, and nowadays it is immense. This paper provides a comprehensive overview of the research published for the last five years. To do so, it analyzes 4,038 records retrieved from the Clarivate Web of Science database, identifying (i) the most prolific authors and their collaboration networks, (ii) the countries and organizations that are leading research on rough sets, (iii) the journals that are publishing most papers, (iv) the topics that are being most researched, and (v) the principal application domains.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.215
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Soft Computing: Theory Innovations and Problem Solving Benefits
dc.subject bibliometrics
dc.subject co-word analysis
dc.subject rough sets
dc.title Rough Sets: A Bibliometric Analysis from 2014 to 2018
dc.type Conference Paper
dc.type.dcmi Text
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