Unveiling University Groupings: A Clustering Analysis for Academic Rankings

George Matlis, Nikos Dimokas () and Petros Karvelis ()
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George Matlis: Department of Informatics, University of Western Macedonia, 52100 Kastoria, Greece
Nikos Dimokas: Department of Informatics, University of Western Macedonia, 52100 Kastoria, Greece
Petros Karvelis: Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece

Data, 2024, vol. 9, issue 5, 1-41

Abstract: The evaluation and ranking of educational institutions are of paramount importance to a wide range of stakeholders, including students, faculty members, funding organizations, and the institutions themselves. Traditional ranking systems, such as those provided by QS, ARWU, and THE, have offered valuable insights into university performance by employing a variety of indicators to reflect institutional excellence across research, teaching, international outlook, and more. However, these linear rankings may not fully capture the multifaceted nature of university performance. This study introduces a novel clustering analysis that complements existing rankings by grouping universities with similar characteristics, providing a multidimensional perspective on global higher education landscapes. Utilizing a range of clustering algorithms—K-Means, GMM, Agglomerative, and Fuzzy C-Means—and incorporating both traditional and unique indicators, our approach seeks to highlight the commonalities and shared strengths within clusters of universities. This analysis does not aim to supplant existing ranking systems but to augment them by offering stakeholders an alternative lens through which to view and assess university performance. By focusing on group similarities rather than ordinal positions, our method encourages a more nuanced understanding of institutional excellence and facilitates peer learning among universities with similar profiles. While acknowledging the limitations inherent in any methodological approach, including the selection of indicators and clustering algorithms, this study underscores the value of complementary analyses in enriching our understanding of higher educational institutions’ performance.

Keywords: clustering of universities; clustering; K-means; GMM; agglomerative; fuzzy C-means; Quacquarelli Symonds (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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