Data mining model for identifying high-quality journals

Abstract

The focus in local universities over the last decade, have shifted from teaching at undergraduate and postgraduate levels to conducting research and publishing in reputed local and international journals. Such publications will enhance the reputation on the individual and the university. The last two decades has seen a rapid rise in open access journals. This has led to quality issues and hence chossing journals for publication has become an issue. Most of these journals focus on the monetary aspect and will publish articles that previously may not have been accepted. Some of the issues include design of the study, methodology and the rigor of the analysis. This has great consequences as some of these papers are cited and used as a basis for further studies. Another cause for concern is that, the honest researchers are sometimes duped, into believing that journals are legitimate and may end up by publishing good material in them. In addition, at present, it is very difficult to identify the fake journals from the legitimate ones. Therefore, the objective of the research was to introduce a data mining model which helps the publishers to identify the highest quality and most suitable journals to publish their research findings. The study focused on the journals in the field of Computer Science. Journal Impact Factor, H-index, Scientific Journal Rankings, Eigen factor Score, Article Influence Score and Source Normalized Impact per Paper journal metrics were used for building this data mining model. Journals were clustered into five clusters using K-Means clustering algorithm and the clusters were interpreted as excellent, good, fair, poor and very poor based on the results.