Clustering-Based Approach for Clustering Journals in Computer Science

Abstract

In the present scientific world, most of the authors of scientific literature are seeking effective ways to share their research findings with large peer groups. But finding a high-quality journal to publish paper is a huge challenge. Most of the journals present today are predatory and less-quality. The main aim of this study is to help the researchers in identifying the quality level of computer science journals by introducing a data mining model based on six journal quality metrics (Journal Impact Factor, SCImago Journal Rank, Eigenfactor, H-index, Source Normalized Impact per Paper, and Article Influence). Further, another objective is to identify the best metrics to measure the quality of journals out of the six attributes. A sample dataset of 200 journals was used and journals were clustered into five clusters using K-means clustering algorithm. When finding the best quality metrics, Pearson's and Spearman's correlation coefficients were calculated. A more accurate clustering model with an accuracy of 0.9171 was developed considering only suitable attributes.