A multimetric clustering approach for unbiased categorization of computer science journals: the framework proposed

Abstract

Purpose The rank of a journal is usually quantified by a single metric like Impact Factor (IF), CiteScore or SCImago Journal Rank (SJR). To avoid bias due to the use of a single metric, a multimetric-based data-centric unsupervised machine learning framework is proposed for journal categorization. Design/methodology/approach This study integrates six bibliometric indicators as features to map the latent structure of journal impact. The proposed framework incorporates two state-of-the-art dimensionality reduction algorithms (PCA and t-SNE), which also contribute to feature engineering. Using PCA, the correlated metrics are transformed into reduced dimensions (2D/3D) that serve as input for k-means clustering. The optimal clusters (k = 4), aligned with IF, Scopus and SJR quartiles, are validated through internal and external evaluation measures. Findings Multimetric-based clustering of 4,146 computer science (CS) journals is compared with established single-metric approaches (SJR, IF and Scopus quartiles) and with HJRS's proprietary multimetric approach, adopted in Pakistan. The average metric-based results provide interpretable outcomes against SJR, IF and Scopus quartiles (single-metric) as well as HJRS (multi-metric). The proposed journal categorization method is fully automated and offers improved classification of journals compared to well-known traditional metrics. Originality/value The contribution of this study is evident in the proposed framework for categorizing journals using multiple metrics. The novelty of our work lies in the integration and domain-specific adaptation of unsupervised machine learning to categorize CS journals. Peer review The peer review history for this article is available at: Link to the website