Journal cross-citation analysis for validation and improvement of journal-based subject classification in bibliometric research

Abstract

The objective of this study is to use a clustering algorithm based on journal cross-citation to validate and to improve the journal-based subject classification schemes. The cognitive structure based on the clustering is visualized by the journal cross-citation network and three kinds of representative journals in each cluster among the communication network have been detected and analyzed. As an existing reference system the 15-field subject classification by Glänzel and Schubert (Scientometrics 56:55-73, 2003) has been compared with the clustering structure. © 2010 Akadémiai Kiadó, Budapest, Hungary.