Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda

Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.

Automated summary

This review conducts a bibliometric analysis to map the academic research on multi-criteria sorting, classification, and clustering methods in multi-criteria decision analysis (MCDA). The results highlight the key research trends and avenues in this field, providing a map of existing evidence and recommending promising avenues for future research. The study contributes to the knowledge in MCDA by identifying influential articles, visualizing trends through network analysis, and highlighting areas for future research.