Machine Learning: A Science Mapping Analysis
- Citation Author(s):
- Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales
- Submitted by:
- Juan Rincon-Patino
- Last updated:
- Tue, 11/12/2019 - 10:38
- Data Format:
Machine learning is becoming increasingly important for companies and the scientific community. It allows us to generate solutions for several problems faced by society. In this study, we perform a science mapping analysis on the machine learning research, in order to provide an overview of the scientific work during the last decade in this area and to show trends that could be the basis for future developments in the field of computer science. This study was carried out using the CiteSpace and SciMAT tools based on results from Scopus and Clarivate Web of Science. This analysis shows how the field has evolved, by highlighting the most notable authors, institutions, keywords, countries, categories, and journals. The results obtained through the analysis provide information on tendencies and on the possible future of machine learning, particularly in areas such as health, biology and banking, where machine learning is an important tool to generate solutions.
The data includes two projects created in SciMAT and CiteSpace, with their respective bibliographical references used as input, to carry out a bibliometric analysis of the evolution of machine learning during the last years.
- Citespace project citespace-project.zip (27.08 MB)
- Results obtained through the analysis made in Citespace citespace-results.zip (19.05 MB)
- Data used as input - First part citespace-data1.zip (20.40 MB)
- Data used as input - Second part citespace-data2.zip (24.01 MB)
- Data used as input - Third part citespace-data3.zip (23.83 MB)