Updating the SCImago journal and country rank classification: A new approach using Ward's clustering and alternative combination of citation measures

Abstract

This study introduces a new proposal to refine the classification of the SCImago Journal and Country Rank (SJR) platform by using clustering techniques and an alternative combination of citation measures from an initial 18,891 SJR journal network. Thus, a journaljournal matrix including simultaneously fractionalized values of direct citation, cocitation, and coupling was symmetrized by cosine similarity and later transformed into distances before performing clustering. The results provided a new cluster-based subject structure comprising 290 clusters that emerge by executing Ward's clustering in two phases and using a mixed labeling procedure based on tf-idf scores of the original SJR category tags and significant words extracted from journal titles. In total, 13,716 SJR journals were classified using this new cluster-based scheme. Although more than 5,000 journals were omitted in the classification process, the method produced a consistent classification with a balanced structure of coherent and well-defined clusters, a moderated multiassignment of journals, and a softer concentration of journals over clusters than in the original SJR categories. New subject disciplines such as "nanoscience and nanotechnology" or "social work" were also detected, providing evidence of good performance of our approach in refining the journal classification and updating the subject classification structure.

Cross-disciplinary collaboration versus coexistence in LIS serials: analysis of authorship affiliations in four European countries

“…Obviously, the accuracy of a journal classification system can heavily influence the results of any study that analyses the level of interdisciplinarity/multidisciplinarity, productivity and research impact in the different scientific disciplines involved in a set of journals classified within the same field. For that reason, the precision of the WoS and Scopus journal classification systems has been a constant matter of concern among researchers, who have analysed several options to validate and improve them (Janssens et al 2009;López-Illescas et al 2009;Zhang et al 2010;Thijs et al 2015;Gómez-Núñez et al 2016;Urbano et al 2005). Despite these efforts, Leydesdorff and Bornmann (2015) explored the use of WoS categories for calculating field-normalized citation impact indicators in the areas of information science and science and technology studies and concluded that the use of this category might seriously harm the quality of the evaluation.…”

Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

“…Alternative tests with other clustering algorithms like k-means, single linkage and complete linkage were executed in R statistical software, but the results were not satisfactory: most of the distributions were skewed, with a few crowded journal clusters and an abundance of small clusters and singletons. Only the Ward method, likewise used in a related study (Gómez-Núñez et al, 2016) gave us acceptable results despite the generation of a couple of superclusters of journals. Therefore, on the basis of previous tests and because of their integration in Pajek and VOSViewer software, the VOS method algorithm was selected and run on the normalized journal network integrating the three citation-based links.…”

A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis

“…As a hierarchical agglomerate cluster algorithm, the WCM has a wide range of applications [19–21]. First, it is started by accepting each node as a separate cluster.…”