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AuthorBakas, Nikolaos P.
AuthorKoutsantonis, Dionisios
AuthorPlevris, Vagelis
AuthorLangousis,
Authorreas
AuthorChatzichristofis, Savvas A.
Available date2024-10-02T05:59:49Z
Publication Date2022
Publication Name13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
ResourceScopus
URIhttp://dx.doi.org/10.1109/IISA56318.2022.9904344
URIhttp://hdl.handle.net/10576/59660
AbstractScientific literature is prosperously evolving, exhibiting exponential growth in the last decades. For a wide range of scientific thematic areas, it is hard or even impossible for individual researchers to analyze in detail the available published works. For this purpose, we utilize a robust multidimensional scaling procedure, to construct the bibliometric maps of the literature, for keywords, authors and references. Particularly, we propose a generic machine learning algorithm for multidimensional scaling, and describe the algorithmic procedure for the generation of the bibliometric maps.
SponsorThe contribution of Andreas Langousis has been conducted within the project PerManeNt, which has been co-financed by the European Regional Development Fund of the European Union and Greek National Funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code: T2EDK-04177).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBibliometrics
Multidimensional Scaling
Optimization
TitleInverse Transform Sampling for Bibliometric Literature Analysis
TypeConference
dc.accessType Full Text


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