A Bibliometric Exploration of Multiple Criteria Decision Aid and Clustering—A Conceptual Taxonomy

Abstract

This work explores the intersection of Multiple Criteria Decision Aid (MCDA) and clustering techniques, revealing unexploited potential and novel perspectives arising from their integration, challenging their conventional separation. It serves as a compass, guiding researchers through a bibliometric exploration and a conceptual taxonomy consolidating existing knowledge. Employing a two-fold methodology, we first sketch the field's contours through a bibliometric lens, uncovering its intellectual structure, thematic landscape, and social dynamics. Then, using science mapping techniques like co-word analysis, historiography, and collaboration network analysis, we examine patterns, revealing an interconnected mosaic of concepts. Our findings unveil a natural grouping into three categories: (1) Mixed-yet-not-integrated approaches, explores sequential applications—clustering followed by MCDA or vice versa—where one method precedes and informs the other. (2) ‘Relational/ordered clustering’ leveraging criteria dependency to refine structures. (3) Using MCDA to improve clustering mechanics through similarity metrics, domain knowledge incorporation, and robustness. We conclusively propose a taxonomy along three axes: Units of Analysis, Instrumentalisation, and Objective. The key takeaway emphasises the collaborative potential of MCDA, envisioning a landscape where the integration of MCDA and clustering not only enhances existing methodologies but also spawns innovative paradigms, fostering a symbiotic relationship that transcends conventional boundaries.