Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63954

Rough Sets: A Bibliometric Analysis from 2014 to 2018

File Size Format  
0174.pdf 1.53 MB Adobe PDF View/Open

Item Summary

Title:Rough Sets: A Bibliometric Analysis from 2014 to 2018
Authors:Heradio, Ruben
Fernandez-Amoros, David
Moral-Muñoz, Jose A.
Cobo, Manuel J.
Keywords:Soft Computing: Theory Innovations and Problem Solving Benefits
bibliometrics
co-word analysis
rough sets
Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63954
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.215
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Soft Computing: Theory Innovations and Problem Solving Benefits


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons