Exploring the ranking, classifications and evolution mechanisms of research fronts: A method based on multiattribute decision making and clustering.

This study aims to present a multiattribute decision-making (MADM) and clustering method to explore the ranking, classifications and evolution mechanisms of the research fronts in the Web of Science Essential Science Indicators (ESI) database. First, bibliometrics are used to reveal the characteristics of the 57 ESI research fronts with more than 40 ESI highly cited papers (ESI-HCPs) for each research front. Second, the eight representative indicators are discovered to get answers to the following two questions: (i) Who publishes the ESI-HCPs that form a research front? and (ii) Where citations to these ESI-HCPs come from on a research front? Next, we investigate the ranking and clusters among the 57 ESI research fronts using the MADM and 𝑘-means clustering method and uncover the evolution process of the research fronts in different clusters based on the representative indicators. We also compare the performances of different countries in these research fronts and find that the USA and China are the leading countries in most research fronts. However, the two countries behave differently with regard to the rankings, the classifications and the evolution.