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Article

Unveiling University Groupings: A Clustering Analysis for Academic Rankings †

1
Department of Informatics, University of Western Macedonia, 52100 Kastoria, Greece
2
Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
*
Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), Volos, Greece, 10–12 July 2023.
Submission received: 5 February 2024 / Revised: 3 May 2024 / Accepted: 6 May 2024 / Published: 11 May 2024

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.

1. Introduction

Finding a distinguished university is a critical step in securing a high-quality education, offering students a sturdy foundation of knowledge, skills, and credentials. Furthermore, it can lead to heightened career prospects post-graduation, as employers tend to favor graduates from prestigious universities, often translating into more enhanced job opportunities. These esteemed universities offer students the chance to participate in cutting-edge research, gain invaluable experiential insights, and cultivate essential skill sets crucial for their forthcoming endeavors.
Evaluating and ranking universities is a complex and multifaceted process that involves careful consideration of various indicators to ensure an objective and precise assessment. Leading ranking organizations, such as the Academic Ranking of World Universities (ARWU) [1], Quacquarelli Symonds (QS) [2], and Times Higher Education World University Rankings (THE) [3], employ sophisticated algorithms to evaluate universities based on a wide range of factors, including academic reputation, faculty–student ratio, academic citations, and international diversity. Each indicator is assigned a specific weight to reflect its relative importance in the university evaluation process. According to [4], an examination of these indicators reveals the following:
  • The low similarity of indicators across ranking lists implies that organizations primarily rely on distinct sets of criteria when assessing universities.
  • The weight assigned to indicate the significance of an indicator can vary across different ranking lists.
  • The research production and impact-related indicators are similar among ranking lists.
University rankings not only aid students in making informed decisions, but also incentivize universities to enhance their performance in critical domains like research, teaching, and internationalization, all of which are essential to their stakeholders. While these rankings offer significant benefits, they are not without limitations, including the following:
  • Subjectivity—The choice of indicators and their associated weights may rely on subjective and contentious criteria, resulting in rankings that do not accurately reflect the genuine capabilities and performance of universities.
  • Narrow Focus—The indicators employed in ranking systems might fall short of encompassing the entire spectrum of activities and outputs of universities, potentially resulting in a limited and partial assessment of their performance. For instance, rankings primarily centered around research productivity may not adequately gauge the quality of teaching or the broader impact of universities on their local communities.
  • Incentive Distortion—Ranking systems can incentivize universities to prioritize specific activities over others, potentially distorting their original missions and objectives.
  • Lack of Transparency—The lack of transparency in the methodology and data sources employed by ranking organizations poses a challenge for universities to pinpoint and address areas for improvement. Additionally, this opacity may foster skepticism and mistrust among stakeholders towards the reliability of the rankings.
Recognizing the limitations of existing ranking systems, this study is motivated by the pursuit of a complementary analytical approach. By employing clustering algorithms to group universities based on similar characteristics, we aim to provide a more nuanced perspective on the global higher education landscape. This clustering analysis is not intended to replace traditional rankings but to augment them, offering interested parties an alternative approach for viewing and assessing the strengths and similarities among groups of universities. Such an approach acknowledges the diversity of university missions and the importance of multiple factors in determining institutional success, thus encouraging a more holistic and collaborative understanding of educational excellence. Our motivations are rooted in the belief that a multidimensional analysis can facilitate more informed decision-making for students, educators, policymakers, and institutions alike, promoting a richer dialogue about the qualities that define leading universities worldwide. This approach may also be preferable to a straightforward ranking of universities from top to bottom for several compelling reasons, such as the following:
  • A limitation of criteria—Ranking methodologies often rely on a narrow set of criteria for assessing university performance, yet certain universities excel in diverse domains of expertise. Consequently, employing clustering methods can highlight their individual strengths and areas of excellence.
  • Diverse needs—Every individual harbors distinct priorities when it comes to selecting a university, encompassing factors like expenses, geographic location, and available academic programs.
  • Avoiding Stigma—Establishing a ranking hierarchy where universities are sorted from the best to the worst can result in the marginalization of lower-ranked institutions. To alleviate this effect and promote a more constructive and inclusive view of universities, clustering them based on specific attributes or areas of expertise can be a valuable alternative.
The remainder of the article is organized as follows: In Section 2, we review other related studies, while Section 3 presents the dataset and the data pre-processing phase. Section 4 presents the methodology, and finally, Section 5 concludes our study.

2. Related Work

Clustering is a machine learning technique that belongs to the category of unsupervised learning and aims to group related objects together into distinct clusters. There are several different types of clustering, each with their own benefits and drawbacks. Some of the types of clustering are as follows:
  • Hierarchical—Utilizing this methodology, clusters are structured within a dendritic, tree-like arrangement, in which each smaller cluster serves as a subset of a more comprehensive cluster. Two fundamental modes of hierarchical clustering are evident: agglomerative [5] and divisive [6]. Within the agglomerative clustering technique, each data point originates within its own distinct cluster, culminating in the eventual amalgamation of all clusters into a unified entity. Conversely, the divisive clustering method commences with all data points situated within a solitary cluster, subsequently undergoing incremental partitioning into smaller clusters through the algorithm.
  • K-Means—One widely employed clustering method is K-Means clustering [7]. In this technique, the dataset is partitioned into K clusters, where K is a modifiable parameter. The objective is to minimize the total sum of squared distances between each data point and its designated cluster center.
  • Fuzzy Clustering—This approach enables data points to exhibit multiple degrees of membership across multiple clusters. Among the field, Fuzzy C-Means (FCM) clustering [8] stands as the most widely utilized algorithm for fuzzy clustering.
  • Density-Based—The density-based clustering technique involves the grouping of data points that are in close proximity within high-density regions, while being separated by regions of lower density. Among these methods, DBSCAN [9], which stands for “Density-Based Spatial Clustering of Applications with Noise”, is the most widely recognized density-based clustering algorithm.
  • Model-Based—This methodology posits a mixture of probability distributions as the origin of the data points. Among model-based clustering techniques, the Gaussian Mixture Model (GMM) algorithm [10] stands as the most prevalent and widely employed approach.
Considerable research has been undertaken in the field of evaluating and ranking academic institutions, spanning universities, departments, and diverse academic domains.
A renowned technique employed for the evaluation of university rankings is rank fusion [11]. Rank fusion, also referred to as meta-ranking, constitutes a procedure for amalgamating the outcomes of multiple university rankings, which include assessments based on multiple factors and criteria, thereby yielding a more encompassing and dependable overall ranking. The process of rank fusion entails the normalization of diverse rankings and their amalgamation through a weighted averaging mechanism, wherein the assigned weights quantify the significance attributed to each respective ranking.
In contrast to individual ranking systems, the process of rank fusion presents several advantages. It fosters a greater degree of stability and dependability in the rating system, diminishing the impact of outliers or inaccuracies within individual rankings. Additionally, it possesses the capacity to consider a broader spectrum of factors or perspectives, thereby furnishing a more comprehensive assessment of the performance or quality of the ranked entities. However, it is imperative to exercise caution when employing rank fusion, as the weights applied to amalgamate the rankings may carry a degree of subjectivity or bias.
A useful method for ranking universities is the Borda Count method [12]. The Borda Count method is a single-winner, voting-based technique for aggregating and consolidating rankings from different sources or criteria. In this method, each university is assigned a score in each individual ranking, typically based on its position in that ranking. The scores are then summed across all rankings, and universities are ranked based on their total scores. The higher a university ranks in an individual ranking, the more points it receives. The Borda Count allows for the integration of diverse indicators into a single composite ranking. It is particularly useful when dealing with university rankings from various organizations, as it provides a way to balance and combine these rankings to create a unified assessment of university performance that reflects the collective preferences of the rankings.
There exists a multitude of scholarly investigations concerning the ranking of universities, drawing inspiration from conventional methodologies such as Data Envelopment Analysis (DEA) [13,14], multicriteria sorting [15], the application of the Pareto Front [16], as well as the aforementioned rank fusion technique. Nevertheless, it is important to acknowledge that each university ranking entity may adopt a distinct ranking framework or devise proprietary methodologies to appraise the performance of individual universities, thereby yielding divergent ranking lists.
For our study, we relied on [17], although [18] is very similar to our analysis. In [17], the authors clustered the top 500 universities from the National Taiwan University (NTU) ranking list. The NTU ranking list is based on eight validated research performance indicators, organized into three main categories, each with a corresponding weight:
  • Research productivity—25%
  • Research impact—35%
  • Research excellence—40%
The outcomes of their clustering experiments, involving 12 and 43 clusters, utilizing the DBSCAN, EM (Expectation-Maximization) [19], and K-Means algorithms, demonstrated a strong similarity to the ranking provided by the NTU ranking list. This similarity was notably pronounced with the EM and K-Means algorithms, while the DBSCAN algorithm did not yield comparable results. Within the 12 and 43 clusters derived from their analysis, a singleton cluster emerged at the top, containing a single university recognized for its exceptional performance. As a result, they concluded that K-Means stands out as the most suitable algorithm for university clustering.
In the study [18], the dataset from Quacquarelli Symonds (QS) for the year 2022 served as the basis for clustering and analysis. The author employed six score indicators, elaborated in Section 3, and opted for GMM as the primary clustering algorithm. Differing from the eight research-oriented indicators employed in [17], the QS indicators encompass a broader spectrum of criteria for evaluating university performance. The determination of the optimal number of clusters, set at four, was accomplished using Akaike’s Information Criteria (AIC) [20] and Bayesian Information Criteria (BIC) [21]. In contrast to QS’s existing ranking system, the study’s findings introduced a novel classification scheme for universities, where each cluster portrayed universities in a manner distinct from their assigned rankings in the QS list.
In [22], the authors classified the top 500 universities into 21 types according to their disciplinary characteristics, using data from the Institute of Higher Education, Shanghai Jiao Tong University. The indicators used for the classification are the percentage of publications in six broad disciplinary areas:
  • Arts/Humanities and Social Sciences
  • Natural Sciences and Mathematics
  • Engineering/Technology and Computer Sciences
  • Life Sciences
  • Clinical Medicine
  • Interdisciplinary and Multidisciplinary Sciences
Universities were categorized based on their focus, priority, and orientation with specific disciplinary groups. The universities that did not fall into any of the aforementioned categories were designated as “balanced”. An examination was conducted to assess the distribution of various types of universities across nations and ranking systems. In the clustering procedure, a customized algorithm was employed, utilizing the Squared Euclidean Distance as the similarity metric.
There are other notable studies related to our work of clustering universities, such as those in [23,24,25,26]. In [23], the object of research is the internal structure of management in universities, its relationship to rating, and the clustering of universities in the Republic of Kazakhstan in order to determine the effectiveness of management. The authors considered three clustering models, each of which presented intriguing results regarding the clustering parameters and their values. In [24], to enable a more suitable and fair comparison of knowledge exchange performance among English institutions, a cluster analysis sought to identify groups of universities based on their structural characteristics that shape knowledge-sharing possibilities and challenges. The study also recognized the criticality of diverse higher educational institutions and the difficulties that arise from clustering them. In [25], the study highlights the universities located in countries that host 90% of the top-ranked universities in Latin America by presenting the findings of a descriptive analysis based on clusters of 85 Latin American universities found in the top 50 positions of the ARWU, SIR Scimago, QS, and Webometrics rankings. For the purposes of performing the descriptive statistical analysis, clusters were constructed by taking into account the frequency of the presence of universities in the top 50 of the four rankings, their location, and the country they belong to. In [26], the purpose of the study was to classify Korean universities according to their research performance, and validate the classifications by comparing their research performance to those that are located in the U.S. As compared to U.S. peers, Korean universities’ research performance was comparable. Furthermore, the study revealed that the classification outcomes produced by the performance-based method were comparable to those of conventional classifications that used predetermined criteria. However, the above studies are limited only to national or regional data.
Compared to the analyses conducted in studies [17,18,22], our study employed K-Means, GMM, Agglomerative, and Fuzzy C-Means algorithms to offer a more comprehensive and nuanced perspective on the dataset. This approach aimed to unveil diverse patterns and structures that may remain concealed when relying on a single algorithm. To ensure a fair and equitable university clustering process, we expanded upon the six indicators utilized in study [18] by incorporating three additional indicators sourced from the ranking list, as detailed in Section 3. In contrast to the methodologies applied in studies [17,22], which predominantly incorporated research-focused indicators, our approach encompassed a broader spectrum of indicators for evaluating university performance. Despite Fuzzy C-Means calculating the probability of a university belonging to multiple clusters, our algorithm ultimately assigned each university to a single cluster.

3. Dataset and Data Pre-Processing

3.1. Dataset

For the purposes of this research, we acquired a dataset from the official website of Quacquarelli Symonds (QS) for the year 2023. In the dataset, there are over 1400 universities from all over the world, including universities from diverse locations in Europe, Asia, and North America. We selected this particular dataset due to the presence of diverse indicators used for the assessment of university performance, in contrast to other university rankings that primarily rely on bibliographic-related metrics.
In total, there are 27 columns in the dataset, but, according to the QS ranking, each university is ranked and assessed using only the six following columns/indicators:
  • Academic Reputation (ar score)—Evaluates the teaching and research quality of the university.
  • Employer Reputation (er score)—Evaluates how competent, innovative, and effective students and graduates are for the employment market.
  • Faculty/student ratio (fsr score)—Evaluates how universities provide students with meaningful access to faculty staff.
  • Citations per faculty (cpf score)—Evaluates the total number of academic citations about the papers published in the last five years.
  • International student ratio (isr score)—Evaluates the ability of a university to attract foreign students.
  • International faculty ratio (ifr score)—Evaluates the ability of a university to attract foreign faculty staff.
The above indicators each, in turn, get a percentage of 40 % , 10 % , 20 % , 20 % , 5 % , and 5 % of the total score, a proportion specified by QS [2].
In our analysis, we included the following three additional indicators:
  • Size—The total number of full-time degree-seeking students.
  • Focus—The broad subject areas of each university, e.g., Arts, Humanities, Engineering and Technology, Natural Sciences, etc.
  • Age Band—The age of each university.
The selection of the three supplementary indicators was premised on their ability to facilitate the categorization of universities according to their respective values, as shown in Table 1. This enables the establishment of distinct and equitable rankings among the institutions, thus providing a more comprehensive evaluation of their performance across multiple dimensions. Each of the three indicators captures a percentage of the total number of universities. All the above indicators have been selected to be applicable to all universities, regardless of their geographical area.

3.2. Data Pre-Processing

Following the description of the indicators used in our analysis, it was important to undertake certain data pre-processing steps as a necessary precursor to the clustering process. As an initial step, the exclusion of universities with missing data in any of the aforementioned nine indicators was essential to ensure equitable evaluation. Subsequently, categorical string values within the “size” and “focus” indicators were mapped into categorical numerical values spanning from 0 to 100. This conversion was conducted in order for the clustering algorithms to function properly, as they receive only numerical values. Lastly, to scrutinize the correlation among the indicators, whether categorical or continuous, we employed the Spearman rank correlation coefficient [28]. The Spearman rank correlation coefficient is a statistical measure that assesses the strength and direction of the monotonic relationship between two variables. It is based on the ranks of the data rather than their actual values. The resulting correlation matrix for the nine indicators is provided in Table 2.
Upon scrutinizing the aforementioned tables, several pertinent observations can be deduced, contributing to the formulation of meaningful conclusions in the context of the present research. The academic and employer reputation indicators have a relatively strong correlation with most of the other indicators and especially with one another, implying that, as the academic reputation of a university increases, employers are more likely to hire students who graduated from that university. Furthermore, a strong correlation is observed in the relationship between the number of international students and the presence of foreign faculty staff. This outcome aligns with expectations, as a university’s acceptance of a substantial number of international students logically corresponds with the recruitment of foreign faculty members. Another strong and obvious correlation emerges between a university’s academic reputation and the quantity of citations per faculty. This alignment is unsurprising, given that a higher volume of citations stemming from a school’s research activities naturally increases the university’s overall academic standing. Noteworthy observations can be made regarding the size, focus, and age band indicators. It is apparent that the size of a university influences its focus, and vice versa, but does not significantly impact the age of the university. Therefore, the presence of an extensive array of faculty areas in a university does not necessarily imply an older age but rather suggests a relatively larger student population. Additionally, upon analyzing their correlation with the other continuous indicators, it is clear that the size, age, and broad subject areas do not exert a significant influence on a university’s overall performance.
Having established the relative significance of the indicators within our cluster analysis, our subsequent action was to define custom indicator weights, which maintain a proportional ratio to the initial weight assignments provided by QS. Before assessing the clustering algorithms, two distinct normalization techniques were considered:
  • We normalize the data and apply indicator weights.
  • We normalize the data without applying indicator weights.
For normalization, we employed the MinMaxScaler method:
x scaled = x x min x max x min .
Each technique was used once per experimentation cycle. One experimentation cycle involves the following steps:
  • Normalizing the data using one of the aforementioned techniques.
  • Clustering the data with different numbers of clusters.
  • Computing the pairwise similarity between all combinations of clustering algorithms using the Rand Index (RI) [29]. The Rand Index measures the similarity between two clustering results by comparing the agreements and disagreements in pairwise cluster assignments.
By calculating the similarity between a pair of clustering algorithms, we gain insights into the degree of proximity between them. However, our objective was to compare the ranking list generated through the amalgamation of clusters produced by a clustering algorithm, with the QS ranking list. Details of how a ranking list is produced from the clustering algorithms are discussed in Section 4. To facilitate this comparison, we created distinct clusters (named QS clusters) to represent the QS ranking list, separate from those produced by the algorithms. The size of these QS clusters corresponded to the size of the clusters generated by the clustering algorithms. Consequently, the members of each QS cluster were organized to match the order of universities as presented in the QS ranking list. For example, if the first cluster (after applying a clustering algorithm) has a size of 100, then the first QS cluster encompasses the first one hundred universities from the QS ranking list. Respectively, the second QS cluster, irrespective of its size, encompasses the consecutive universities following the top one hundred universities from the first cluster. This approach allowed us to essentially compare the QS ranking list, which is represented by the QS clusters, with the rankings produced by the clustering algorithms.

4. Clustering Analysis

The clustering algorithms that were used for this analysis are the following:
  • K-Means—K-Means can be a useful algorithm for university clustering when simplicity, efficiency, and intuitive results are important.
  • GMM—GMM was selected due to its capability to model the data as coming from a mixture of Gaussian distributions, which is appropriate given the continuous nature of our data. GMM provides a probabilistic model that not only assigns memberships to clusters but also accounts for the covariance structure of the data, thus allowing for a more nuanced understanding of the relationships between universities. GMM was also utilized in a previous study [18], which is relevant to our own research.
  • Agglomerative—Agglomerative clustering can be used when a hierarchy of clusters is needed, or when flexibility and interpretability are important. However, it may not be the best choice for all situations, particularly when the underlying data have complex relationships or non-linear distributions.
  • Fuzzy C-Means—Fuzzy C-Means was chosen for its ability to allow each university to belong to multiple clusters with varying degrees of membership. This method is particularly useful in capturing the inherent overlaps in university characteristics, which are too complex to be neatly partitioned into crisp clusters.
K-Means and GMM initialized their centroids using the K-Means++ method, which selects initial cluster centroids using sampling based on an empirical probability distribution of the points’ contribution to the overall inertia. This technique sped up convergence. The algorithm implemented was “Greedy K-Means++”. Fuzzy C-Means initialized the centroids randomly.
To find the optimal number of clusters using the nine indicators we considered the following:
  • The elbow method for a maximum of 30 clusters using the K-Means algorithm to calculate the sum of squared distances from each point to its assigned center.
  • The AIC and BIC for a maximum of 30 clusters. The maximum number of clusters was chosen arbitrarily. Since AIC and BIC are both statistical measures used for model selection and comparison, we used the GMM algorithm for these measures.
By applying the elbow method, the graph in Figure 1 came up. By taking a closer look at the graph, it is apparent that the elbow method does not work well because, as we will see later on, the data are not very clustered. As a result, there is a smooth curve and the optimal value of K is unclear, thus making this method unfit for use.
Using the AIC and BIC measures we came up with the graph that is shown in Figure 2. The number of clusters for the minimum value of BIC is seven, which is identical to the results of the EM algorithm. The number of clusters for the minimum value of AIC is approximately 29 but we used 24, as the difference between the AIC value of 29 and 24 clusters is quite small. In order to visualize the clusters, we used the Multidimensional Scaling (MDS) algorithm [30] to reduce the initial dimensions of the data to two.
The clusters were ordered according to the mean value of each cluster ( C M ). The mean value of a cluster was calculated as follows:
C M = j = 1 cluster size i = 1 9 indicator j , i 9 cluster size .
We calculated a university’s overall performance by summing up its individual indicator values and then dividing the total by nine, the number of indicators, to derive the mean score. Next, by aggregating the means of all universities within a cluster and dividing this sum by the cluster’s size, we obtained the cluster’s mean score. This methodology generates a cluster ranking that subsequently reflects the ranking of universities within each respective cluster.

4.1. Clustering without Weights for Seven Clusters

The clustering results for seven clusters and nine indicators without weights are shown in Figure 3. The sizes of each cluster for every algorithm are shown in Table 3. Table 4 presents the similarity between every pair of algorithms.
By visualizing the results of the clustering algorithms and calculating the Rand Index (see Table 4), we can make a few observations. First of all, the cluster with the highest mean (the red cluster), for every algorithm, has a very low density. That is to be expected given that universities in that cluster tend to have diverse values for their indicators, whereas universities in clusters with lower means tend to have similar values for these indicators. Thus, clusters with a lower mean have a higher density.
It is important to note that the clusters exhibit overlap, meaning that universities belonging to one cluster may appear in others. This overlap is a result of two key factors. Firstly, the clustering process relies on nine distinct indicators, while the visualization phase reduces these indicators to just two dimensions. This dimensionality reduction can lead to errors in accurately representing the data. Secondly, the differentiation between the cluster members of a clustering algorithm and the QS clusters contributes to the observed overlap. For the sake of comparison, we assumed that the cluster members of a clustering algorithm mirrored those of the QS clusters (the clusters created to compare the QS ranking list to the ranking list produced by the clustering algorithms). This assumption implies that, regardless of their size, each cluster of a clustering algorithm will encompass the same members (universities) as its corresponding counterpart of the QS clusters. In practice, however, the cluster members of a clustering algorithm often differed from those of the QS clusters. In other words, a university may find itself within a cluster featuring a lower mean, while, according to the clustering of QS, it should have been placed in a cluster with the highest mean, or vice versa. As a result, some universities may appear in different clusters, according to the QS clusters. In order to compare the clusters derived from the clustering methods with those of the QS, we selected the algorithm that demonstrated the highest similarity to the QS, as defined by the Rand Index.
Table 5 illustrates the difference among the cluster members of Fuzzy C-Means and QS, and Figure 4 provides a visual representation corresponding to the findings presented in Table 5. The Cluster Differentiation (CD) row in Table 5 enumerates the difference between the cluster members of Fuzzy C-Means and QS clusters. For example, the first cluster has a CD of 15, implying that there are 15 universities in the first cluster of QS that are not present in the first Fuzzy C-Means cluster. In Figure 4, the university nodes are delineated by two colors. The left color designates the cluster to which a university belongs according to the results of Fuzzy C-Means, while the right color designates the cluster a university is expected to be a part of based on the clustering of QS. The complete/detailed list of clusters for all clustering algorithms can be found in Table A1.
In terms of university distribution within the clusters, the K-Means algorithm and the Agglomerative method exhibit considerable similarity. The outcomes of one align closely with the results of the other, and their Rand Index demonstrates a relatively high level of agreement compared to other algorithms. Fuzzy C-Means shares similarities with K-Means and Agglomerative methods, but upon closer examination through the graph and size table, it becomes evident that Clusters 5 and 6 contain significantly fewer universities than their counterparts in the other two methods. Interestingly, the number of universities in the final cluster of Fuzzy C-Means exceeds those in K-Means and Agglomerative by a considerable margin. GMM stands out distinctly from all other algorithms, characterized by an exceptionally large size of the first cluster, and notably, the smallest size of the last cluster in comparison to corresponding clusters in the other algorithms. Furthermore, GMM reveals that a significant number of universities from the higher mean clusters are intermixed with universities from clusters of lower means to a degree not observed in the other clusters.
To assess the significance of each indicator to the clustering process, we employed the following steps:
  • We aggregated the results of K-Means, GMM, Agglomerative, and Fuzzy C-Means to determine the cluster each university belongs to according to the prevailing consensus among the clustering methods. This consensus is determined by identifying the cluster assignment that is most frequently agreed upon by the different clustering algorithms. In other words, we identify the cluster that is most commonly assigned to a university across the various clustering methods.
  • We computed the Spearman correlation coefficient between each indicator and the cluster assignment of universities.
Table 6 shows the strength and nature of the correlation between an indicator and a university’s cluster. The nature of the correlation is negative, which indicates a reverse relationship between cluster assignments and indicator values. Some universities have high scores across the nine indicators and are assigned to clusters, such as Cluster 1, characterized by high mean values. Conversely, when universities are assigned to clusters with lower mean values, their individual indicator values tend to decrease. This negative correlation between cluster assignment and indicator values suggests that, as universities are grouped into clusters with lower mean values, such as Cluster 6, their performance, as measured by the indicators, diminishes. Regarding the strength of the correlation, certain indicators, such as focus, academic and employer reputation, citations per faculty, and international faculty and student ratios, showcase a strong correlation with cluster assignments. These indicators likely experience a considerable decrease when universities are assigned to clusters with lower mean values.

4.2. Clustering without Weights for 24 Clusters

The clustering results for 24 clusters and nine indicators without weights are shown in Figure 5 and Figure 6. According to Table 7, the Rand Index between every pair of algorithms has significantly increased and the results are now closer to QS. As the number of clusters increases, there are more possible ways to assign universities to clusters. Consequently, the probability of random agreement between two clustering results also increases. Since the Rand Index considers both the number of agreements and disagreements, it tends to increase with the number of clusters.
Moreover, Table 8 presents the cluster sizes, while Table 9 lists the differentiation between cluster members of Agglomerative and QS clusters for 24 clusters without weights.

4.3. Clustering with Weights for Seven Clusters

The clustering results for seven clusters and nine indicators multiplied with weights are shown in Figure 7. The academic reputation indicator carries the highest weight as it exerts the most substantial correlation on the other indicators. In the university categorization process, the size, age, and focus indicators have each been assigned a weight of 0.1. This assignment reflects the fact that these indicators were not considered by the QS in their university rankings. The initial weight values for the faculty-to-student ratio (fsr) and citations per faculty (cpf) indicators were set at half the weight of the academic reputation indicator. By assigning a weight of 0.125, we have maintained this initial proportion. To avoid bias against universities that may not admit or have the legal capacity to accept foreign students or faculty, the international student and faculty ratio indicators were assigned smaller weight values.
The Rand Index (Table 10) between the weighted clustering, utilizing nine indicators multiplied by weights, and the unweighted clustering shows a relatively modest change. Specifically, we observe a slight increase in the similarity between the Fuzzy C-Means algorithm and Agglomerative, which results in an improvement in the similarity between Agglomerative and QS. K-Means exhibits a higher similarity with QS.
In terms of the configuration and size of the clusters (Table 11), the graphs reveal that clusters generated by the K-Means, Agglomerative, and Fuzzy C-Means algorithms are more distinctly separated in the weighted approach, making it easier to discern the distribution of universities. However, this improvement does not extend to the GMM algorithm, in which clusters remain mixed. The differentiation between cluster members of K-Means and QS clusters for seven clusters with weights is presented in Table 12 and visualized in Figure 8. The indicators and their corresponding weights are shown in Table 13.
Table 14 shows the correlation of the indicators with the cluster assignments using the weights specified in Table 13. The correlations of the size and age indicators have notably increased compared to their correlations in Table 6, when weights were not considered. Additionally, the correlations of the academic reputation, employer reputation, and faculty/student ratio indicators have shown an increase. This suggests that, as the cluster assignment increases, the indicator values now decrease to a greater extent than before, when weights were not applied. However, the correlation of the citations per faculty indicator remains unchanged, while the correlation of the international faculty and student ratio indicators has decreased in relation to the cluster assignment.

4.4. Clustering with Weights for 24 Clusters

In the case of clustering with 24 clusters in Figure 9, there are no noteworthy observations regarding the K-Means and Agglomerative methods. In the case of Fuzzy C-Means, it is notable that results are provided for only 20 clusters (Table 15). This is due to the presence of four clusters with zero universities. This is not surprising, given that there are already two clusters that have only one university. The specific identity of these clusters with zero universities does not carry particular significance, as universities are generally assigned to a cluster before the calculation of cluster means. The calculation of the mean value of each cluster is the key determinant of a cluster’s significance, and, in this context, clusters with zero universities are excluded from that process.
The differentiation between cluster members of K-Means and QS clusters for 24 clusters with weights is presented in Table 16 and visualized in Figure 10. Finally, according to Table 17, K-Means demonstrates a higher similarity with QS for 24 clusters with weights.

4.5. Discussion

Among the methods tested, Fuzzy C-Means emerged as the most promising method for seven clusters without weights, exhibiting the highest Rand Index (RI). We decided to opt for seven clusters over 24 clusters due to interpretability considerations, as fewer clusters are easier to interpret. Additionally, clustering methods with weights were not chosen as promising methods due to their arbitrary nature. Although the QS ranking system has established predetermined weights for its indicators, there are numerous ways to adjust these weights while maintaining their original ratio. This highlights the importance of selecting appropriate clustering methods and cluster numbers based on both performance metrics and interpretability requirements.
This study’s primary insight lies in revealing the positioning of universities into clusters, as explicitly outlined in Table A1. Interestingly, despite being expected to reside within the same cluster according to the QS ranking, some universities are instead placed in a distant cluster, characterized by a significantly different cluster mean. This understanding underscores that these universities exhibit greater similarity to the universities within their current cluster than to those they were expected to be grouped with according to the QS ranking. This information can aid universities in assessing their performance by offering a different perspective on their rankings.
While the current study has provided valuable insights into clustering universities, it is important to acknowledge its limitations. A significant limitation lies in the use of only nine indicators. With a broader range of indicators, we could obtain more accurate and comprehensive data results. Another limitation arises from the time-dependent nature of university rankings. QS publishes university rankings annually, potentially assigning different ranks to individual universities, and different weights to the indicators each year. Consequently, the clustering process must be conducted every year. Finally, a limitation is related to the visualization of the data and, more precisely, the case where different universities seem to overlap across different clusters. This is because of the transition from nine to two dimensions.

5. Conclusions

Rankings offer a clear and straightforward way to compare universities, while clustering provides a more nuanced understanding of the diversity and similarities within the higher education landscape. Instead of a strict numerical ranking, clustering provides a more comprehensive view by highlighting similarities and differences between groups of universities, allowing for better comparisons and assessments. Clustering analysis introduces a dynamic aspect to rankings, highlighting that institutions can move between clusters based on their performance over time. Moreover, grouping universities may impact their reputation positively or negatively, depending on the cluster they belong to. Institutions in higher-ranked clusters may experience an improvement in their global reputation, attracting more students, faculty, and research collaborations.
In this article, we conducted a thorough analysis of university ranking data and concluded that clustering methods can be applied to group institutions with similar characteristics and provide a ranking between different groups. The latter is achieved by ordering clusters based on the performance of institutions within them. When examining the distribution of institutions within each cluster, discernible differences emerged among clusters generated through different algorithms, varied cluster numbers, or the inclusion of adjusted weights, as opposed to those formed without incorporating weights. Moreover, a comprehensive analysis of the features utilized in the clustering analysis revealed the degree of correlation and importance of each feature in assessing an institution’s performance. Overall, creating a hierarchical ranking that ranks universities from best to worst or relying on a narrow set of indicators to assess an institution’s performance may restrict the richness of results. Alternatively, leveraging clustering methods allows us to overcome these limitations and achieve a more nuanced understanding.
Table A1 illustrates the distribution of universities across clusters based on their profile similarities. It demonstrates that our method groups institutions not by their ordinal rankings, but by their shared attributes and challenges, enhancing the comprehension of what constitutes institutional excellence beyond mere numerical ranking. The consistency in cluster membership, as shown in Table A1, provides empirical evidence that our clustering approach enables a deeper and more stable understanding of universities’ characteristics, aligning with our goal of facilitating peer learning among similarly profiled institutions.
Opportunities for future research abound in refining and expanding upon the current study’s findings. One avenue for exploration involves further refinement of distance functions used in assessing university similarity or proximity, with a focus on tailoring these functions to better capture nuances in university performance. Moreover, future research could delve into the exploration of additional indicators and weights influencing university clustering, such as geographical location, academic disciplines, or institutional characteristics. To enrich the clustering process, we might consider incorporating supplementary indicators from alternative sources or organizations. By integrating these elements into the clustering framework, researchers can gain deeper insights into the complex dynamics of university groupings and their implications for various stakeholders in the higher education landscape. A more comprehensive analysis could extend beyond our clustering process to delve into the question of which indicators and their corresponding values lead certain universities to be grouped into clusters with means different from those anticipated based on QS rankings. Moving forward, we intend to consider the implications of the Variance Inflation Factor (VIF) more thoroughly in future work, exploring methods to integrate this important aspect of data pre-processing. This will enable us to refine our model further and enhance the interpretability and validity of our findings.

Author Contributions

Conceptualization, N.D. and P.K.; methodology, N.D., P.K., and G.M.; software, G.M.; validation, G.M.; formal analysis, G.M., N.D., and P.K.; investigation, G.M. and N.D.; resources, G.M.; data curation, G.M.; writing—original draft preparation, G.M., P.K., and N.D.; writing—review and editing, N.D., P.K., and G.M.; visualization, G.M.; supervision, N.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The table presents the universities grouped by cluster for each clustering algorithm alongside their corresponding QS cluster without using weights. Within the table, the QS columns represent the cluster assignment based on the QS clustering method, while the remaining columns denote the cluster assignments according to the Fuzzy C-Means, K-Means, GMM, and Agglomerative clustering methods.
Table A1. The table presents the universities grouped by cluster for each clustering algorithm alongside their corresponding QS cluster without using weights. Within the table, the QS columns represent the cluster assignment based on the QS clustering method, while the remaining columns denote the cluster assignments according to the Fuzzy C-Means, K-Means, GMM, and Agglomerative clustering methods.
UniversityRankQSFuzzyQSKMQSGMMQSAGG
Massachusetts Institute of Technology (MIT)111111211
University of Cambridge211111111
Stanford University311111111
University of Oxford411111111
Harvard University511111111
California Institute of Technology (Caltech)6=11111211
Imperial College London6=11111111
UCL811111111
ETH Zurich—Swiss Federal Institute of Technology911111211
University of Chicago1011111111
National University of Singapore (NUS)1111111111
Peking University1211111111
University of Pennsylvania1311111111
Tsinghua University1411121111
The University of Edinburgh1511111111
EPFL16=11111211
Princeton University16=11111211
Yale University1811111111
“Nanyang Technological University, Singapore (NTU)”1911111111
Cornell University2011111111
The University of Hong Kong2111111111
Columbia University2211111112
The University of Tokyo2311121111
Johns Hopkins University2411111111
University of Michigan-Ann Arbor2511121111
Université PSL2611111211
“University of California, Berkeley (UCB)”2711111111
The University of Manchester2811111111
Seoul National University2911121111
The Australian National University3011111111
McGill University3111111111
Northwestern University3211121111
The University of Melbourne3311111111
Fudan University34=11111111
University of Toronto34=11111111
Kyoto University3611121111
King’s College London3711111111
The Chinese University of Hong Kong (CUHK)3811111111
New York University (NYU)3911121112
The Hong Kong University of Science and Technology4011111211
The University of Sydney4111111111
KAIST—Korea Advanced Institute of Science & Technology42=11121211
Zhejiang University42=11111111
“University of California, Los Angeles (UCLA)”4411121111
The University of New South Wales (UNSW Sydney)4511111111
Shanghai Jiao Tong University4611121111
University of British Columbia4711111111
Institut Polytechnique de Paris4811111211
Technical University of Munich4911111212
Duke University50=11121112
The University of Queensland50=11111111
Carnegie Mellon University5211111111
“University of California, San Diego (UCSD)”5311111111
City University of Hong Kong5411111211
Tokyo Institute of Technology (Tokyo Tech)5511121211
The London School of Economics and Political Science (LSE)5611111211
Monash University5711111111
University of Amsterdam5811111111
Ludwig-Maximilians-Universität München5911121112
Sorbonne University6011121114
Delft University of Technology61=11111211
University of Bristol61=11111111
Brown University6311111112
The University of Warwick6411111111
Ruprecht-Karls-Universität Heidelberg65=11121112
The Hong Kong Polytechnic University65=11111211
Universidad de Buenos Aires (UBA)6711121112
Osaka University6811121111
Université Paris-Saclay6911121211
Universiti Malaya (UM)7011121112
Pohang University of Science And Technology (POSTECH)7113121214
University of Texas at Austin7211121112
Yonsei University7311121112
Korea University7411121112
Lomonosov Moscow State University7511121112
KU Leuven7611111111
National Taiwan University (NTU)7711121112
University of Southampton7811111111
Tohoku University7913121112
University of Washington8011121112
University of Glasgow8111111111
University of Copenhagen8211121112
University of Wisconsin-Madison83=11121112
University of Zurich83=11111112
University of Illinois at Urbana-Champaign8511121112
University of Leeds8611111111
The University of Auckland8711111111
Georgia Institute of Technology8811111211
KTH Royal Institute of Technology8911111212
The University of Western Australia9011111111
University of Birmingham9111111111
Durham University9211111111
Pennsylvania State University9311121112
University of Science and Technology of China9413141214
Lund University9511111111
The University of Sheffield96=11111111
University of St Andrews96=11111111
“Trinity College Dublin, The University of Dublin”9811111111
Sungkyunkwan University (SKKU)9913121112
Rice University10011111121
University of Oslo10111111122
“University of California, Davis”102=11111121
“University of North Carolina, Chapel Hill”102=13121122
Technical University of Denmark104=11111222
Universidad Nacional Autónoma de México (UNAM)104=13121122
King Abdulaziz University (KAU)106=11111122
University of Helsinki106=11121222
Boston University10811111121
The University of Adelaide10911111121
University of Alberta11011111121
École Normale Supérieure de Lyon11111141224
Nagoya University112=13121122
Utrecht University112=11241124
University of Nottingham11411211121
Universidade de São Paulo11513221122
Aalto University116=12211223
Université de Montréal116=11211122
Freie Universitaet Berlin118=11221122
Washington University in St. Louis118=11211122
University of Bern12012211222
Pontificia Universidad Católica de Chile (UC)12111221122
Newcastle University12211211121
Universiti Putra Malaysia (UPM)12311221122
Wageningen University & Research12413251222
Chalmers University of Technology125=11211222
Queen Mary University of London125=11211121
University of Geneva125=11211121
Uppsala University12811211121
Purdue University129=11221122
Universiti Kebangsaan Malaysia (UKM)129=13221122
Humboldt-Universität zu Berlin131=11221122
Leiden University131=11211121
Nanjing University13311241124
University of Southern California13411211121
Kyushu University13513221122
University of Basel13611211121
University of Technology Sydney13712211223
Eindhoven University of Technology13812211222
Politecnico di Milano13921221221
The Ohio State University14021221122
Hokkaido University141=23221122
“KIT, Karlsruhe Institute of Technology”141=21211221
Ghent University143=21221122
Universiti Sains Malaysia (USM)143=23221122
University of Groningen14521211121
Lancaster University14622211121
RWTH Aachen University147=21211121
University of Rochester147=21211122
“University of California, Santa Barbara (UCSB)”14922241224
Al-Farabi Kazakh National University15023221122
University of Vienna15121211121
McMaster University15221211122
Stockholm University15322231221
University of Waterloo15422211223
Emory University155=23251122
Indian Institute of Science155=23241224
Hanyang University15723221122
Technische Universität Berlin (TU Berlin)15821221222
Michigan State University15921221122
King Fahd University of Petroleum & Minerals16021231122
Aarhus University16122241124
University of York16222211121
The University of Exeter16322211121
Texas A&M University164=24221122
“University of Maryland, College Park”164=23241122
Cardiff University16621211121
Alma Mater Studiorum—University of Bologna167=24221122
Universidad de Chile167=23221122
Eberhard Karls Universität Tübingen16923251122
Tecnológico de Monterrey17021221122
Sapienza University of Rome17124221122
Indian Institute of Technology Bombay (IITB)172=23221222
Western University172=22211123
Ecole des Ponts ParisTech174=23251222
Indian Institute of Technology Delhi (IITD)174=23221222
Case Western Reserve University17623251122
National Tsing Hua University17723221222
Universitat Autònoma de Barcelona17824241122
Technische Universität Wien179=22211223
University of Bath179=22211221
Khalifa University of Science and Technology181=22231223
University College Dublin181=22211121
University of Pittsburgh181=23251125
Universitat de Barcelona18424221122
University of Gothenburg185=22231122
University of Minnesota Twin Cities185=23221122
University of Wollongong185=22231123
University of Florida18823221122
Albert-Ludwigs-Universitaet Freiburg18922241124
RMIT University190=22211223
University of Liverpool190=22211121
“The University of Newcastle, Australia (UON)”19222241124
Curtin University19322231223
Wuhan University19422241124
Macquarie University195=22211123
Université catholique de Louvain (UCLouvain)195=22211124
Keio University197=23221122
Ulsan National Institute of Science and Technology (UNIST)197=23241224
Vanderbilt University19923251122
Technische Universität Dresden20023221122
Rheinische Friedrich-Wilhelms-Universität Bonn20123251122
National Yang Ming Chiao Tung University20223221124
Universiti Teknologi Malaysia203=23221222
University of Lausanne203=22331223
Dartmouth College205=23341124
Waseda University205=23321222
University of Bergen20722351122
Erasmus University Rotterdam208=22311223
Qatar University208=22331123
Universidade Estadual de Campinas (Unicamp)210=24341122
Universite libre de Bruxelles210=22311123
Tongji University212=22341124
University of Twente212=22331223
Vrije Universiteit Amsterdam21422341124
Universidad Autónoma de Madrid215=23321122
University of Göttingen215=23351122
Harbin Institute of Technology217=23341224
University of Otago217=22331124
Arizona State University21922341224
Universidad de los Andes220=23321122
University of Aberdeen220=22311123
Queensland University of Technology (QUT)222=22331223
The Hebrew University of Jerusalem222=23351122
Chulalongkorn University224=23321122
National Cheng Kung University (NCKU)224=23321122
Complutense University of Madrid226=23321122
Southern University of Science and Technology226=23351224
Universität Hamburg22823341124
University of Reading22922331223
Bauman Moscow State Technical University23023351422
Gadjah Mada University23123321122
Radboud University23222341124
Queen’s University Belfast233=22331123
Universitat Pompeu Fabra (Barcelona)233=22341223
Bandung Institute of Technology (ITB)235=23321222
“University of California, Irvine”235=22331123
King Saud University237=22331122
University of Cape Town237=22321134
University of Ottawa237=22311133
University of Sussex240=22331133
USI—Università della Svizzera italiana240=22331233
University of Calgary24222331133
Universidad Nacional de Colombia243=24321132
Università di Padova243=24341132
University of Notre Dame243=23321232
Queen’s University at Kingston246=22321134
Universitas Indonesia248=21321132
Université Paris Cité248=24341234
Indian Institute of Technology Madras (IITM)25024341232
Vrije Universiteit Brussel (VUB)25122331132
American University of Beirut (AUB)25222331132
University of Massachusetts Amherst253=22341234
University of Navarra253=23321132
University of Virginia253=23321132
Loughborough University256=22331233
Mahidol University256=23351135
Universiti Brunei Darussalam (UBD)256=22331233
Sciences Po25923351235
Novosibirsk State University260=23351235
Tel Aviv University260=24341134
Beijing Normal University262=24341234
The University of Arizona262=23351132
Indian Institute of Technology Kanpur (IITK)264=24341234
Tomsk State University264=23351435
Deakin University26622331133
Moscow Institute of Physics and Technology (MIPT/Moscow Phystech)267=23351235
Rutgers University–New Brunswick267=23321232
Sun Yat-sen University267=23341132
Indian Institute of Technology Kharagpur (IIT-KGP)270=27341234
Kyung Hee University270=23351135
National University of Ireland Galway270=22331132
Saint Petersburg State University270=23351132
University of Porto27424341132
Technical University of Darmstadt275=22331233
Victoria University of Wellington275=22331233
L.N. Gumilyov Eurasian National University (ENU)27723351435
Maastricht University27822331333
University of Leicester27922331133
University of Antwerp28022331133
Georgetown University281=23321132
Heriot-Watt University281=22331233
Hong Kong Baptist University281=22331233
Graz University of Technology284=23351235
Taylor’s University284=23331233
UCSI University284=22331233
University of Canterbury | Te Whare Wānanga o Waitaha28422331233
University of Warsaw284=23321232
Belarusian State University288=23351435
Charles University288=23321132
Gwangju Institute of Science and Technology (GIST)288=24341234
University of Turku29123351132
Massey University29222331233
Jagiellonian University293=23321132
University of Tasmania293=22331333
RUDN University29523351135
Swinburne University of Technology296=22331233
United Arab Emirates University296=22331132
University of Miami296=23351132
University of Tartu296=23351132
Griffith University300=22331133
Université Paris 1 Panthéon-Sorbonne300=24321236
Xi’an Jiaotong University30224341134
University College Cork30322331132
University of Macau30422331233
University of Surrey30522331333
Huazhong University of Science and Technology30624341134
Tianjin University30724341234
Dalhousie University308=22331133
HSE University308=23351435
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)308=23351235
Universität Innsbruck308=22331132
North Carolina State University312=22341234
Tufts University312=23351132
University of Tsukuba312=23351135
La Trobe University31622331333
Université Grenoble Alpes317=24341134
University of Colorado Boulder317=23341234
University of Illinois at Chicago (UIC)317=23361132
Linköping University320=22331233
Universidad Carlos III de Madrid (UC3M)320=23351435
Kazan (Volga region) Federal University32223351435
Pontificia Universidad Católica Argentina32323351135
University of Milan32424341136
Politecnico di Torino325=27341234
University of Strathclyde325=22331233
National Taiwan University of Science and Technology (Taiwan Tech)32723351237
Goethe-University Frankfurt am Main328=23361134
Simon Fraser University328=22331333
Aalborg University33022331133
University of Waikato33122331333
National Taiwan Normal University33223351235
Universidade Federal do Rio de Janeiro33324361136
National University of Sciences And Technology (NUST) Islamabad33423351235
University of Cologne335=23341134
University of Lisbon335=24341134
Ural Federal University—UrFU335=23351435
Hiroshima University33823351135
Indiana University Bloomington33923341134
Friedrich-Alexander-Universität Erlangen-Nürnberg340=24341134
Universiti Teknologi Brunei340=22351232
University of East Anglia (UEA)34222331133
“Birkbeck, University of London”343=22331333
Universitat Politècnica de Catalunya · BarcelonaTech (UPC)343=23351235
MGIMO University34523351435
Ewha Womans University34623351135
IE University347=22331233
University of Jyväskylä347=23351235
University of Southern Denmark (SDU)347=22331233
Johannes Kepler University Linz350=22351232
University of Connecticut350=22341134
Norwegian University of Science And Technology352=22341134
University of Dundee35422331133
Beijing Institute of Technology355=27341234
“City, University of London”355=22331333
Universität Stuttgart355=24341334
“University of Chemistry and Technology, Prague”35823351235
ITMO University359=23351435
University of Victoria (UVic)359=22331333
Universiti Teknologi PETRONAS (UTP)36122331333
George Washington University36233341134
Kobe University363=33351135
Pontificia Universidad Católica del Perú363=37371237
Quaid-i-Azam University363=35341234
University of South Australia363=32331333
Virginia Polytechnic Institute and State University363=32341134
Lincoln University36832331233
Airlangga University369=33321132
American University of Sharjah369=32331333
Indian Institute of Technology Roorkee (IITR)369=37341234
Umea University369=33351135
Universidade Nova de Lisboa369=34341134
University of Kansas369=33351132
“University of California, Santa Cruz”375=32341234
University of Kent375=32331333
University Ulm375=33351235
Czech Technical University in Prague378=33351435
Nankai University378=34341134
Sharif University of Technology380=37341234
University of Hawai’i at Mānoa380=33341134
Peter the Great St. Petersburg Polytechnic University382=33351435
Pontificia Universidad Javeriana382=34321137
Indian Institute of Technology Guwahati (IITG)384=36341234
Sultan Qaboos University384=32331132
Taipei Medical University (TMU)384=33351237
Westfälische Wilhelms-Universität Münster384=33361132
Lappeenranta-Lahti University of Technology LUT388=37341234
Northeastern University388=32431333
Universidad de Palermo (UP)390=33451435
Chung-Ang University (CAU)392=33451135
Tokyo Medical and Dental University (TMDU)392=33451235
University of Oulu392=32441134
University of Utah392=33451132
Shandong University396=34441134
National Research Tomsk Polytechnic University398=33451435
Tilburg University398=32431233
Universitat Politècnica de València400=33451235
Vilnius University400=33451135
Colegio de México402=33451435
Royal Holloway University of London402=32431333
University of Pisa40434461136
Satbayev University40533451435
Sichuan University406=34441634
South China University of Technology406=34441634
Colorado State University408=33451245
Technion—Israel Institute of Technology408=32441144
HUFS—Hankuk (Korea) University of Foreign Studies410=33451445
Julius-Maximilians-Universität Würzburg410=34441144
Brunel University London412=32431343
University of Johannesburg412=32431243
University of the Philippines412=33461142
Tampere University41534441144
Ruhr-Universität Bochum416=34441144
“Stony Brook University, State University of New York”416=33431144
The American University in Cairo416=32431242
University of Naples—Federico II416=34441146
Johannes Gutenberg Universität Mainz420=33461144
National Technical University of Athens422=37441244
Shanghai University422=33451245
Xiamen University422=34441144
Flinders University425=32431143
Swansea University425=32431142
University at Buffalo SUNY425=32441143
National Sun Yat-sen University428=34471247
“University of Colorado, Denver”428=33451145
University of Science and Technology Beijing428=36441644
University of Witwatersrand428=32441144
Universidad Austral43233451445
Université Laval43332431142
Far Eastern Federal University434=33451445
Université de Strasbourg434=34461144
National Taipei University of Technology436=34471247
Università Vita-Salute San Raffaele436=33451245
Oxford Brookes University438=32431243
University of Coimbra438=34441146
Wake Forest University438=33451145
Universidade Federal de São Paulo441=33451145
Universität des Saarlandes441=33451145
Amirkabir University of Technology443=37441244
Auezov South Kazakhstan University (SKU)443=33451445
Beihang University (former BUAA)443=34441644
Illinois Institute of Technology443=34431344
SOAS University of London443=32432343
Washington State University443=32442144
Bogor Agricultural University449=33452445
Hasselt University449=33452242
Umm Al-Qura University449=32432142
Universidad de Montevideo (UM)449=33452145
“University of California, Riverside”45332442144
Stellenbosch University454=34442146
University of Tromsø The Arctic University of Norway454=33452142
York University45632432343
Institut National des Sciences Appliquées de Lyon (INSA)457=33432244
Sogang University457=33452447
University of Trento457=34442244
University of Florence46034442146
James Cook University461=32442144
Rensselaer Polytechnic Institute461=34442344
Southeast University461=34442644
Universidad de Belgrano461=33452445
“Essex, University of”465=32432343
Universidad de Santiago de Chile (USACH)465=34462147
The National University of Science and Technology MISIS467=33452245
Universidad de La Habana467=33452445
University of Iowa467=33462142
Dublin City University471=32432143
Tulane University471=33452145
University of Cyprus (UCY)473=32432244
University of Saskatchewan473=32432143
Chang Gung University475=33452245
University of Turin475=35462146
Imam Abdulrahman Bin Faisal University (IAU) (formerly UNIVERSITY OF DAMMAM)477=32452142
Koç University477=33462147
UNESP477=34462146
Bond University481=32432143
Dongguk University481=33452145
Iowa State University481=35442344
Kazakh National Agrarian University KazNAU481=33452445
Universiti Utara Malaysia (UUM)481=37472445
Auckland University of Technology (AUT)486=32432343
University of Klagenfurt486=32432243
Ajou University488=33452145
Universidad Politécnica de Madrid (UPM)488=37472247
Aix-Marseille University490=34462146
Ben-Gurion University of The Negev490=32442144
Chiba University490=33452145
Justus-Liebig-University Giessen490=34442144
The Catholic University of Korea494=33452745
Universidad ORT Uruguay494=33452445
University of Granada494=34462146
Brandeis University497=33452245
Jilin University497=33462142
Central South University499=35442644
University of Rome Tor Vergata499=37472247
Western Sydney University501–51032432143
Bar-Ilan University501–51034442144
Colorado School of Mines501–51037442244
Kyungpook National University501–51033452145
Middle East Technical University501–51035462247
Missouri University of Science and Technology501–51032442244
Université de Montpellier501–51037442344
University of Aveiro501–51037442644
University of St.Gallen (HSG)501–51032432243
University of Stirling501–51032432344
University of Tehran501–51036442246
Yokohama City University501–51033452745
Abai Kazakh National Pedagogical University511–52033452445
Florida State University511–52034462146
“Goldsmiths, University of London”511–52032432343
Universidad de Alcalá511–52033452144
Università Cattolica del Sacro Cuore511–52037472447
University of Bayreuth511–52033452243
University of Canberra511–52032432343
“University of Missouri, Columbia”511–52033562144
Altai State University521–53033552445
The New School521–53033552445
Université de Liège521–53034542144
University of Bordeaux521–53037562246
University of Delhi521–53035562146
University of Texas Dallas521–53034542144
Warsaw University of Technology521–53037572247
Hitotsubashi University531–54037572247
Inha University531–54033552145
Sabanci University531–54034572347
Saint Joseph University of Beirut (USJ)531–54032532142
“Universidad Central “Marta Abreu” de Las Villas”531–54033552745
University of Balamand531–54033552445
University of Limerick531–54032532143
Canadian University Dubai541–55032532343
East China Normal University541–55034542344
Savitribai Phule Pune University541–55033552245
Southern Federal University541–55033552445
Universidad Nacional de La Plata (UNLP)541–55034562146
Universidad Panamericana (UP)541–55033552445
Universität Konstanz541–55032532243
Universität Mannheim541–55037542244
V. N. Karazin Kharkiv National University541–55033552444
Cairo University551–56035562146
Concordia University551–56032532343
Jeonbuk National University551–56033552145
Masaryk University551–56034532344
National Research Saratov State University551–56033552445
Northwestern Polytechnical University551–56034542645
Sejong University551–56034542347
Universidad de Zaragoza551–56033562145
University of Eastern Finland551–56034572247
University of Szeged551–56033552144
Almaty Technological University561–57033552445
Applied Science University—Bahrain561–57032532343
Aston University561–57032532343
Bilkent University561–57032572343
Boston College561–57034562346
Dalian University of Technology561–57035542644
Murdoch University561–57032532343
Niigata University561–57033552745
Singapore Management University561–57032532343
“Sofia University “St. Kliment Ohridski””561–57033552445
Universidad de Sevilla561–57034562146
Università degli Studi di Pavia561–57034542146
University of Electronic Science and Technology of China561–57036542644
University of Ulsan561–57033552145
Hallym University571–58033552745
Holy Spirit University of Kaslik571–58032532142
Nagasaki University571–58033552745
Universitat de Valencia571–58035562146
Université de Fribourg571–58032532343
Université du Québec571–58034542144
Indian Institute of Technology Hyderabad581–59037542647
Macau University of Science and Technology581–59032532343
National Central University581–59034572347
Shenzhen University581–59033552645
Université Paul Sabatier Toulouse III581–59037572247
University of Delaware581–59034542344
University of Massachusetts Boston581–59037542644
China Agricultural University591-60044542647
Hunan University591-60046542644
Lehigh University591-60044542344
Politecnico di Bari591-60047572644
Sungshin Women’s University591-60043552745
University of Crete591-60045542147
University of Guelph591-60044542244
University of Jordan591-60044562144
University of Minho591-60044562145
University of Pretoria591-60044562146
Abo Akademi University601–65044542344
Al Ain University601–65042532343
Bangor University601–65042532144
Carleton University601–65044542354
Chiang Mai University601–65045562156
East China University of Science and Technology601–65047542654
Edith Cowan University601–65042532353
Gifu University601–65043552755
Immanuel Kant Baltic Federal University601–65043552455
Istanbul Technical University601–65047572257
Ivane Javakhishvili Tbilisi State University601–65043552755
Kanazawa University601–65043552155
“Kingston University, London”601–65042532353
Lahore University of Management Sciences (LUMS)601–65044572557
Lebanese American University601–65042532153
Lebanese University601–65044562154
Leibniz University Hannover601–65044562254
“Lingnan University, Hong Kong”601–65042532353
Management and Science University601–65042532353
National and Kapodistrian University of Athens601–65045562156
National Chengchi University601–65044572457
Okayama University601–65043552755
Oregon State University601–65044562356
Osaka City University601–65043552755
Pontifícia Universidade Católica do Rio de Janeiro601–65047573257
Pusan National University601–65044563155
Renmin (People’s) University of China601–65047573257
Samara National Research University (Samara University)601–65043553455
St. Louis University601–65043553755
Sunway University601–65043573457
The University of Georgia601–65044563156
“The University of Tennessee, Knoxville”601–65044543156
Ulster University601–65042533353
Universidad Anáhuac México601–65043553455
Universidad de Concepción601–65044563557
Universidad Pontificia Bolivariana601–65043553455
Universidad Pontificia Comillas601–65047573257
Universität Bremen601–65044563354
Universität Potsdam601–65044573354
Université Claude Bernard Lyon 1601–65044563156
University of Ljubljana601–65044563156
University of Milano-Bicocca601–65047573654
University of Salamanca601–65044563157
University of Sharjah601–65042533353
University of South Florida601–65044543354
Wayne State University601–65043553155
Aberystwyth University651–70042533354
Abu Dhabi University651–70042533353
Ahlia University651–70042533353
Ajman University651–70042533353
Alfaisal University651–70042533353
American University in Dubai651–70042533353
Aristotle University of Thessaloniki651–70045563156
Ateneo de Manila University651–70044563157
Central Queensland University (CQUniversity Australia)651–70042533353
China University of Geosciences651–70047573657
Chongqing University651–70047543654
Comenius University in Bratislava651–70047553457
Coventry University651–70042533353
Drexel University651–70044563156
Gunma University651–70043553755
Indian Institute of Technology (BHU) Varanasi651–70047543654
International Islamic University Malaysia (IIUM)651–70044563154
Karaganda State Technical University651–70043553455
Karl-Franzens-Universitaet Graz651–70044563354
Konkuk University651–70043553155
Kumamoto University651–70043553755
National Chung Hsing University651–70046563355
“National Technical University “Kharkiv Polytechnic Institute””651–70043553455
New Jersey Institute of Technology (NJIT)651–70042533353
Northumbria University at Newcastle651–70042533353
O.P. Jindal Global University651–70043573457
Palacký University Olomouc651–70044573356
Plekhanov Russian University of Economics651–70047573457
Prince Mohammad Bin Fahd university651–70042533353
S.D. Asfendiyarov Kazakh National Medical University651–70043573455
Sechenov University651–70043553455
Soochow University651–70047543654
Taras Shevchenko National University of Kyiv651–70045563457
Thammasat University651–70044563156
Universidad de Antioquia651–70045563556
Universidad Externado de Colombia651–70047573257
Universidad ICESI651–70043653755
Universidad Peruana Cayetano Heredia (UPCH)651–70043653755
Universitat Ramon Llull651–70042633253
Universiti Teknologi MARA—UiTM651–70045663557
University of Debrecen651–70044663154
University of Genoa651–70045663156
University of Huddersfield651–70042633353
University of Hull651–70042633154
University of Nebraska—Lincoln651–70044643354
University of Plymouth651–70044643154
University of Southern Queensland651–70042633353
American University701–75045673457
American University of the Middle East701–75042633353
Boğaziçi University701–75046663557
Brno University of Technology701–75047673454
Charles Darwin University701–75042633353
City University of New York701–75045663156
Eötvös Loránd University701–75047663256
Free University of Bozen-Bolzano701–75042633353
Institut Teknologi Sepuluh Nopember (ITS Surabaya)701–75047673457
Jinan University (China)701–75042633154
Jouf University701–75042633153
Kagoshima University701–75043653755
Khoja Akhmet Yassawi International Kazakh-Turkish University701–75043653155
King Khalid University701–75042633153
“National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute””701–75047673457
Pavol Jozef Šafárik University in Košice701–75043653154
Princess Nourah bint Abdulrahman University701–75042633353
Ritsumeikan University701–75044663257
Southern Cross University701–75042633353
Stevens Institute of Technology701–75044633354
Tallinn University of Technology (TalTech)701–75047673357
Tokushima University701–75043653755
Tokyo Metropolitan University701–75047673657
Tokyo University of Agriculture and Technology701–75047673657
Universidad Católica del Uruguay (UCU)701–75044673457
Universidad de La Sabana701–75044663557
Universidad Iberoamericana IBERO701–75044663157
Universidad San Francisco de Quito (USFQ)701–75043673157
Universidade Federal de Minas Gerais701–75045663156
Universiti Tenaga Nasional (UNITEN)701–75047673257
University of Bradford701–75042633354
University of Cincinnati701–75044663156
University of Haifa701–75047673657
University of Kentucky701–75044663156
University of Mons701–75047673357
University of New Brunswick701–75042633354
University of New Mexico701–75044663156
University of Oklahoma701–75044663156
University of Oregon701–75047663656
University of Pecs701–75044663154
University of Portsmouth701–75042633353
University of South Carolina701–75044663156
University of the Basque Country701–75047663655
University of Trieste701–75045643154
University of Vermont701–75043663155
University of Westminster701–75042633353
Victoria University701–75042633353
Vilnius Gediminas Technical University701–75047673457
Virginia Commonwealth University701–75043663155
Zayed University701–75042633353
Beijing University of Technology751–80047663655
Belarusian National Technical University (BNTU)751–80047673457
Chonnam National University751–80044663155
Chungnam National University751–80044663155
Clark University751–80044673357
CY Cergy Paris University751–80047633263
Dankook University751–80043653765
Florida International University751–80044663166
Howard University751–80053653765
Instituto Politécnico Nacional (IPN)751–80057673567
Instituto Tecnológico Autónomo de México (ITAM)751–80052673267
Instituto Tecnológico de Buenos Aires (ITBA)751–80054673467
Keele University751–80052633164
Lanzhou University751–80057663666
Lobachevsky University751–80057653467
“Manipal Academy of Higher Education, Manipal, Karnataka, India”751–80054663165
Memorial University of Newfoundland751–80054663164
Michigan Technological University751–80054673667
Middlesex University751–80052633363
Osaka Prefecture University751–80054653767
Paris Lodron University of Salzburg751–80052633363
Philipps-Universität Marburg751–80054663166
Pontificia Universidad Católica de Valparaíso751–80057663567
Riga Technical University751–80054673464
Saint Petersburg Electrotechnical University ETU-LETI751–80057653467
Shiraz University751–80057643664
Syracuse University751–80057663367
Temple University751–80055663166
Universidad Adolfo Ibáñez751–80057673467
Universidad Autónoma Chapingo751–80053653765
Universidad Católica Andres Bello751–80057673567
Universidad de la República (Udelar)751–80054663166
Universidad de San Andrés—UdeSA751–80057673467
Universidad del Rosario751–80057673567
Universidade de Santiago de Compostela751–80057663666
Universidade Federal do Rio Grande Do Sul751–80055663166
Universitas Padjadjaran751–80057673466
Universitat Rovira i Virgili751–80055663167
Université Côte d∖’Azur751–80054663164
Université de Lille751–80057673267
Université de Sherbrooke751–80054663164
Université de Sousse751–80053653765
University of Denver751–80054653767
University of Houston751–80057663366
University of Hyderabad751–80055663667
University of Siena751–80055663166
Academician Y.A. Buketov Karaganda University801–100057673467
“Adam Mickiewicz University, Poznań”801–100057663666
AGH University of Science and Technology801–100057663765
Ain Shams University801–100054663166
Australian Catholic University801–100052633363
Beijing Foreign Studies University801–100057673467
Beijing Jiaotong University801–100057673667
Beijing University of Posts and Telecommunications801–100057673667
Beirut Arab University801–100052633163
Bournemouth University801–100052633363
Budapest University of Technology and Economics801–100057673367
Ca’ Foscari University of Venice801–100057673366
Catania University801–100056663166
Chandigarh University801–100057673467
Chang Jung Christian University801–100057673767
Charles Sturt University801–100057633363
Clarkson University801–100054643364
Clemson University801–100057663366
College of William and Mary801–100057674367
Cracow University of Technology (Politechnika Krakowska)801–100057674567
Czech University of Life Sciences in Prague801–100057674364
De La Salle University801–100057664567
De Montfort University801–100052634363
Diponegoro University801–100055664166
Donghua University801–100067674667
Duy Tan University801–100067674364
Edinburgh Napier University801–100062634363
Gdańsk University of Technology801–100067674767
Georgia State University801–100067664366
German Jordanian University801–100067674363
Gulf University for Science and Technology801–100062634363
Hacettepe University801–100066664566
Harbin Engineering University801–100067674667
Indian Institute of Technology Bhubaneswar801–100067674667
Indiana University–Purdue University Indianapolis801–100064664164
Instituto Tecnológico de Santo Domingo (INTEC)801–100067674467
International Christian University801–100062634363
Islamic University of Madinah801–100062634363
Istanbul University801–100065664166
“ITESO, Universidad Jesuita de Guadalajara”801–100067674767
Jamia Millia Islamia801–100067674667
Jordan University of Science & Technology801–100067674467
Kansas State University801–100064664364
Kasetsart University801–100067664566
Kaunas University of Technology801–100067674467
Kazakh-British Technical University801–100067674567
Khon Kaen University801–100065664566
King Faisal University801–100062634363
King Mongkut’s University of Technology Thonburi801–100067674567
Kyrgyz-Turkish Manas University801–100062634363
Liverpool John Moores University801–100064674364
Lodz University of Technology801–100067674767
London Metropolitan University801–100062634363
London South Bank University801–100062634363
Louisiana State University801–100067664666
Loyola University Chicago801–100065664166
Lviv Polytechnic National University801–100067664765
Manchester Metropolitan University (MMU)801–100064664364
Maynooth University801–100062634363
Mendel University in Brno801–100067674467
Nanjing University of Aeronautics and Astronautics801–100067674667
National Chung Cheng University801–100067674467
Nicolaus Copernicus University801–100064664765
NJSC KIMEP University801–100062634363
Northwest University (China)801–100064654765
Notre Dame University-Louaize NDU801–100062634363
Nottingham Trent University801–100062634364
Novosibirsk State Technical University801–100067674467
Oklahoma State University801–100064664164
Perm State National Research University801–100067674767
Pondicherry University801–100067674667
Pontificia Universidad Católica del Ecuador (PUCE)801–100065664166
Pontifícia Universidade Católica de São Paulo801–100065664167
Poznań University of Technology801–100067674767
Prince of Songkla University801–100065664566
Princess Sumaya University for Technology801–100067674363
Qassim University801–100062634163
“Queen Margaret University, Edinburgh”801–100062634363
Riga Stradins University801–100067674464
Ritsumeikan Asia Pacific University801–100062634363
Robert Gordon University801–100062634363
Russian Presidential Academy of National Economy and Public Administration801–100067674767
Russian-Armenian (Slavonic) State University801–100067674364
Rutgers University–Newark801–100067674367
Saint-Petersburg Mining University801–100067674467
Shinshu University801–100065664767
Shoolini University of Biotechnology and Management Sciences801–100067674367
Slovak University of Technology in Bratislava801–100067674767
Sophia University801–100064664466
South Ural State University (National Research University)801–100067674465
Southern Methodist University801–100067674667
Sumy State University801–100064654164
Széchenyi István University801–100066674167
Szent Istvan University801–100067674467
Technical University of Kosice801–100067674467
Technical University of Liberec801–100067674467
Technological University Dublin801–100067674364
Tecnológico de Costa Rica -TEC801–100067674567
Tokyo University of Science801–100067674567
TU Dortmund University801–100067664366
Ufa State Aviation Technical University801–100067674765
Universidad Autónoma del Estado de Hidalgo (UAEH)801–100063664765
Universidad Autónoma del Estado de México (UAEMex)801–100066664566
Universidad Autónoma Metropolitana (UAM)801–100066664567
Universidad de Guadalajara (UDG)801–100065664166
Universidad de las Américas Puebla (UDLAP)801–100074664167
Universidad de Los Andes—(ULA) Mérida801–100075664166
Universidad de los Andes—Chile801–100075664567
Universidad del Valle801–100076664566
Universidad Diego Portales (UDP)801–100075664567
Universidad EAFIT801–100077674567
Universidad Nacional de Córdoba—UNC801–100077664566
Universidad Simón Bolívar (USB)801–100077674567
Universidad Torcuato Di Tella801–100077674467
Universidade Católica Portuguesa—UCP801–100076664167
Universidade de Brasília801–100075664166
Universidade Federal de Santa Catarina801–100075664166
Universidade Federal de São Carlos (UFSCar)801–100075664166
Universidade Federal do Paraná—UFPR801–100075664166
Universita’ degli Studi di Ferrara801–100076664666
Università degli Studi di Perugia801–100075664666
Università degli studi Roma Tre801–100077664667
Universita’ Politecnica delle Marche801–100077674667
Universitas Brawijaya801–100076664566
Universität Duisburg-Essen801–100074664164
Université de Lorraine801–100077664366
Université de Nantes801–100077664366
Université de Rennes 1801–100077674367
Universiti Malaysia Pahang801–100077674367
Universiti Malaysia Perlis801–100077674767
Universiti Pendidikan Sultan Idris (UPSI)801–100077674467
Universiti Tunku Abdul Rahman (UTAR)801–100076664567
University at Albany SUNY801–100077664666
University of Alicante801–100077674667
University of Baghdad801–100074664765
University of Bahrain801–100072634363
University of Bari801–100076664666
University of Brescia801–100077674667
University of Brighton801–100072634363
University of Calcutta801–100077664366
University of Central Florida801–100075664166
University of Central Lancashire801–100074634363
University of Dhaka801–100075664566
University of Dubai801–100072634363
University of East London801–100072634363
University of Engineering & Technology (UET) Lahore801–100077674567
UNIVERSITY OF GDANSK801–100077674667
University of Greenwich801–100072634363
University of Hartford801–100077675467
University of Hertfordshire801–100072635363
University of Hohenheim801–100077675367
University of Hradec Kralove801–100077675467
University of Kwazulu-Natal801–100074665166
University of Lincoln801–100072635364
University of Lodz801–100077665665
University of Louisville801–100074665164
University of Malta801–100074675165
University of Maribor801–100075665165
“University of Maryland, Baltimore County”801–100074665164
University of Messina (UniME)801–100075665666
University of Mississippi801–100075665765
University of Modena and Reggio Emilia801–100075665666
University of Murcia801–100074665166
University of New England Australia801–100077675667
University of New Hampshire801–100077665666
University of Parma801–100076665666
University of Patras801–100076665166
University of Salford801–100077635364
University of Santo Tomas801–100074665164
University of Seoul801–100077675767
University of the Punjab801–100077665567
University of the West of England801–100074635363
University of Tulsa801–100077675667
University of Tyumen801–100077675467
University of Wroclaw801–100077665465
University of Wyoming801–100077675667
University of Zagreb801–100075665166
University of Žilina801–100077675767
Verona University801–100076665667
Viet Nam National University Ho Chi Minh City (VNU-HCM)801–100077665577
“Vietnam National University, Hanoi”801–100077675577
Vytautas Magnus University801–100077675477
Worcester Polytechnic Institute801–100074675377
Wroclaw University of Science and Technology (WRUST)801–100077675477
Yamaguchi University801–100074655777
Yerevan State University801–100077665775
Yeungnam University801–100074665174
Yokohama National University801–100077675377
Al Quds University The Arab University in Jerusalem1001–120074675177
Alexandria University1001–120076665176
Aligarh Muslim University1001–120077665776
Amity University1001–120077675377
Amrita Vishwa Vidyapeetham1001–120074665177
Ankara Üniversitesi1001–120076665176
An-Najah National University1001–120074665174
Asia University Taiwan1001–120077675677
Assiut University1001–120075665176
Athens University of Economics and Business1001–120077675277
Auburn University1001–120077665676
Azerbaijan State University of Economics1001–120077675777
Babes-Bolyai University1001–120077665476
Baku State University1001–120077675777
Banaras Hindu University1001–120076665176
Baylor University1001–120077665776
Belarusian State University of Informatics and Radioelectronics1001–120077675477
Benemérita Universidad Autónoma de Puebla1001–120076665576
Bielefeld University1001–120077665376
Bina Nusantara University (BINUS)1001–120077675477
Binghamton University SUNY1001–120077665676
“Birla Institute of Technology and Science, Pilani”1001–120077665376
Birmingham City University1001–120074635373
BRAC University1001–120077675577
Brigham Young University1001–120077665676
Brock University1001–120077665376
Canterbury Christ Church University1001–120077675376
CEU Universities1001–120077665477
Chungbuk National University1001–120075665775
COMSATS University Islamabad1001–120077665577
Corvinus University of Budapest1001–120077675477
CUNY The City College of New York1001–120076665677
Doshisha University1001–120077665476
Escuela Politécnica Nacional1001–120077675777
Escuela Superior Politécnica del Litoral (ESPOL)1001–120077675577
Financial University under the Government of the Russian Federation1001–120077675777
Fordham University1001–120077665676
Future University in Egypt1001–120077775477
Gazi Üniversitesi1001–120076765576
George Mason University1001–120077765676
Glasgow Caledonian University1001–120077775374
Harper Adams University1001–120077775377
Huazhong Agricultural University1001–120077765676
Imam Mohammad Ibn Saud Islamic University—IMSIU1001–120077775376
Istanbul Aydin University1001–120077775374
Ivan Franko National University of Lviv1001–120077765775
Jeju National University1001–120076765777
Kangwon National University1001–120076765777
Kazakh Ablai Khan University of International Relations and World Languages1001–120077775777
Kazan National Research Technological University1001–120077775477
Kent State University1001–120077765376
Kharkiv National University of Radio Electronics1001–120077775477
Kookmin University1001–120077775475
Kuwait University1001–120075765174
Kyoto Institute of Technology1001–120077775677
Kyushu Institute of Technology1001–120077775677
Leeds Beckett University1001–120077775374
Marquette University1001–120077776777
Mendeleev University of Chemical Technology1001–120077776777
Mississippi State University1001–120077766676
Multimedia University (MMU)1001–120077776477
Mustansiriyah University1001–120074766175
Mutah University1001–120076776174
Mykolas Romeris University1001–120077776477
Nagoya Institute of Technology (NIT)1001–120077776677
National Taiwan Ocean University1001–120077776677
National University of Kyiv-Mohyla Academy (NaUKMA)1001–120077776777
North South University1001–120077776577
North-West University1001–120077766677
Ocean University of China1001–120076766677
Odessa I.I. Mechnikov National University1001–120077776777
Ohio University1001–120076766176
Paul Valéry University Montpellier1001–120077776477
Rochester Institute of Technology (RIT)1001–120077766676
Saitama University1001–120077776677
San Diego State University1001–120077766676
Sathyabama Institute of Science and Technology (deemed to be university)1001–120077776777
Seattle University1001–120077776477
Seoul National University of Science and Technology1001–120077776677
Shahid Beheshti University (SBU)1001–120077776677
Shanghai International Studies University1001–120077776477
Sheffield Hallam University1001–120077776374
“Siberian Federal University, SibFU”1001–120077776373
Siksha ’O’ Anusandhan (Deemed to be University)1001–120077776777
Silesian University of Technology1001–120077776777
Sookmyung Women’s University1001–120077776777
Taibah University1001–120072736173
Tallinn University1001–120077776373
Telkom University1001–120077776577
Texas Tech University1001–120075766176
Thapar Institute of Engineering & Technology1001–120077776677
The Herzen State Pedagogical University of Russia1001–120077776477
“The National Research University “Belgorod State University””1001–120077776477
The University of Alabama1001–120077766576
The University of Lahore1001–120077776577
The University of Northampton1001–120077736373
Tokai University1001–120076766577
Tomas Bata University in Zlin1001–120077776377
Ton Duc Thang University1001–120077776377
Universidad Andrés Bello1001–120076766577
Universidad Austral de Chile1001–120075766576
Universidad Autónoma de Nuevo León1001–120076766576
Universidad de Castilla-La Mancha1001–120076766677
Universidad de Lima1001–120077776577
Universidad de Monterrey (UDEM)1001–120077776577
Universidad de Talca1001–120077776577
Universidad de Valparaíso (UV)1001–120076766577
Universidad del Norte1001–120075766576
Universidad del Pacífico1001–120077776577
Universidad Industrial de Santander—UIS1001–120076766576
Universidad Nacional Agraria la Molina1001–120077776777
Universidad Nacional de Cuyo1001–120075766176
Universidad Nacional de la Asunción1001–120075766176
“Universidad Nacional, Costa Rica”1001–120077776577
Universidad Peruana de Ciencias Aplicadas1001–120077766577
Universidad Rey Juan Carlos1001–120077766377
Universidad Técnica Federico Santa María (USM)1001–120077776577
Universidad Tecnológica de Panamá (UTP)1001–120077776477
Universidade da Coruña1001–120077776777
Universidade de Vigo1001–120077776677
Universidade do Estado do Rio de Janeiro (UERJ)1001–120076766576
Universidade Federal de Pernambuco (UFPE)1001–120076766576
Università degli Studi della Tuscia (University of Tuscia)1001–120077776677
Università degli Studi di Udine1001–120077776677
Universitas Hasanuddin1001–120076766576
Universitas Sebelas Maret1001–120076766577
Universität Siegen1001–120077766376
Université de Toulouse II-Le Mirail1001–120077766476
Université Lumière Lyon 21001–120077776477
Université Toulouse 1 Capitole1001–120077776474
Universiti Kuala Lumpur (UniKL)1001–120075766777
Universiti Malaysia Sabah (UMS)1001–120076766177
Universiti Malaysia Sarawak (UNIMAS)1001–120074766177
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Universiti Tun Hussein Onn University of Malaysia (UTHM)1001–120077776677
“University of Agriculture, Faisalabad”1001–120077766376
University of Arkansas Fayetteville1001–120077776677
University of Belgrade1001–120076766176
University of Bucharest1001–120077766576
University of Calabria1001–120077766676
University of Derby1001–120077776374
University of Kufa1001–120075766775
University of Latvia1001–120075766174
University of Miskolc1001–120077776777
University of Missouri Saint Louis1001–120077776677
“University of Missouri, Kansas City”1001–120077776677
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University of Mumbai1001–120076766176
University of Naples Parthenope1001–120077776677
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University of Rhode Island1001–120077766676
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Yildiz Technical University1001–120077766476
Don State Technical University1201–140077766476
Akdeniz Üniversitesi1201–140077766777
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Anadolu University1201–140077776777
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British University in Egypt1201–140077776777
California State University—Los Angeles1201–140077766776
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Dokuz Eylül Üniversitesi1201–140077767777
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Florida Atlantic University—Boca Raton1201–140076767776
Fu Jen Catholic University1201–140076767176
Fundación Universidad De Bogotá-Jorge Tadeo Lozano1201–140077777777
Gebze Yüksek Teknoloji Enstitüsü (GYTE)1201–140077777777
German University in Cairo1201–140077777577
Hanoi University of Science and Technology1201–140077777576
Helwan University1201–140077767777
Hongik University1201–140077777477
Humboldt State University1201–140077777777
Illinois State University1201–140077777777
Indiana State University1201–140077777777
International Islamic University Islamabad (IIU)1201–140077777777
Irkutsk State University1201–140077777777
Istanbul Bilgi Üniversitesi1201–140077777477
Izmir Institute of Technology (IZTECH)1201–140077777777
Kindai University (Kinki University)1201–140076767576
King Mongkut∖’s Institute of Technology Ladkrabang1201–140077777577
Kwansei Gakuin University1201–140077767376
Lucian Blaga University of Sibiu1201–140077767777
Makerere University1201–140076767176
Mansoura University1201–140076767576
Marmara University1201–140077767776
Meiji University1201–140077767476
Miami University1201–140077767676
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Russian State Agrarian University—Moscow Timiryazev Agricultural Academy1201–140077777777
Russian State University for the Humanities1201–140077777777
Saken Seifullin Kazakh Agrotechnical University1201–140077777777
San Francisco State University1201–140077767676
Shanghai University of Finance and Economics1201–140077777577
Shibaura Institute of Technology1201–140077777377
Slovak University of Agriculture in Nitra1201–140077777677
Soochow University (Taiwan)1201–140077777777
Soongsil University1201–140077777377
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SRM INSTITUTE OF SCIENCE AND TECHNOLOGY1201–140077767777
Stefan cel Mare University of Suceava1201–140077777474
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Suranaree University of Technology1201–140077777677
Tamkang University1201–140077777677
Tanta University1201–140077777776
Technical University of Cluj-Napoca1201–140077777777
The Hashemite University1201–140076767177
“The University of Notre Dame, Australia”1201–140077777777
The University of Texas at Arlington1201–140077767376
Toraighyrov University1201–140077777777
Transilvania University of Brasov1201–140076767776
Tunghai University1201–140077767676
Universidad Autónoma de Baja California1201–140077767776
Universidad Autónoma de Chile1201–140077777577
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Universidad Católica de Colombia1201–140077777777
Universidad Católica de La Santísima Concepción—UCSC1201–140077767777
Universidad Católica del Norte1201–140076767577
Universidad Central de Chile1201–140077777777
Universidad de Cartagena1201–140076767776
Universidad de Cuenca1201–140076767776
Universidad de Guanajuato1201–140077767576
Universidad de La Frontera (UFRO)1201–140077777677
Universidad de La Salle1201–140077777777
Universidad de La Serena1201–140077777777
Universidad de las Fuerzas Armadas ESPE (Ex-Escuela Politécnica del Ejército)1201–140077767776
Universidad de Medellín1201–140077777777
Universidad de Panama1201–140077777576
Universidad de Piura1201–140077777577
Universidad de Puerto Rico1201–140076767576
Universidad de Sonora1201–140077767776
Universidad del Bío-Bío1201–140077767576
Universidad del Cauca1201–140077767476
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Universidad del Magdalena1201–140077767776
Universidad del Salvador1201–140077777477
Universidad del Valle de Mexico (UVM)1201–140077777576
Universidad La Salle (ULSA)1201–140076767577
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Universidad Nacional de Quilmes1201–140077777777
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Universidad Popular Autónoma del Estado de Puebla (UPAEP)1201–140077777777
Universidad San Ignacio de Loyola1201–140077777577
Universidad Tecnica Particular De Loja (UPTL)1201–140077777576
“Universidad Tecnológica de la Habana José Antonio Echeverría, Cujae”1201–140077777777
Universidad Tecnológica de Pereira1201–140077767776
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Universidade Federal de Viçosa (UFV)1201–140076767776
Universidade Federal do Ceará (UFC)1201–140076767776
Universidade Federal do Parà—UFPA1201–140076767776
Universidade Federal Fluminense1201–140076767576
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“Universita’ degli Studi “G. d’Annunzio” Chieti Pescara”1201–140077777677
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Université Jean Moulin Lyon 31201–140077777477
Université Paris-Nanterre1201–140077767576
University of Babylon1201–140076767777
University of Colombo1201–140077767576
University of Ghana1201–140076767576
University of International Business and Economics1201–140077777477
University of Karachi1201–140077767576
University of Kragujevac1201–140077777777
University of North Carolina at Charlotte1201–140077767676
University of North Carolina at Greensboro1201–140077767676
University of Silesia in Katowice1201–140077767776
University of Split1201–140077767777
University of Texas El Paso1201–140077777677
University POLITEHNICA of Bucharest1201–140077777777
“University Politehnica of Timisoara, UPT”1201–140077777777
VSB—Technical University of Ostrava1201–140077777477
Western Washington University1201–140077767576
Yarmouk University1201–140076767177
Youngsan University1201–140077777777
Yuan Ze University1201–140077777677
Zagazig University1201–140077767777
Ataturk University1401+77767776
Cukurova University1401+77767777
Damascus University1401+77767776
Erciyes Üniversitesi1401+77767777
Sakarya University1401+77767177
Sudan University of Science and Technology1401+77767777
“Universidad Católica Boliviana “San Pablo””1401+77777777
Universidad Católica de Santiago de Guayaquil1401+77777777
Universidad Tecnológica de Bolívar1401+77777577
Université Mohammed V de Rabat1401+77767777
University of Oradea1401+77767176

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Figure 1. Elbow method for a maximum of 30 clusters using the K-Means algorithm.
Figure 1. Elbow method for a maximum of 30 clusters using the K-Means algorithm.
Data 09 00067 g001
Figure 2. AIC and BIC values for a maximum of 30 clusters and a minimum of two. Reprinted with permission from Ref. [27].
Figure 2. AIC and BIC values for a maximum of 30 clusters and a minimum of two. Reprinted with permission from Ref. [27].
Data 09 00067 g002
Figure 3. Visualization for seven clusters without weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Figure 3. Visualization for seven clusters without weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Data 09 00067 g003
Figure 4. Fuzzy C-Means for seven clusters without weights. The color of the sixth cluster in Figure 3 has been altered from its original light green to pink to enhance visual interpretability.
Figure 4. Fuzzy C-Means for seven clusters without weights. The color of the sixth cluster in Figure 3 has been altered from its original light green to pink to enhance visual interpretability.
Data 09 00067 g004
Figure 5. Visualization for 24 clusters without weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Figure 5. Visualization for 24 clusters without weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Data 09 00067 g005
Figure 6. Agglomerative for 24 clusters without weights.
Figure 6. Agglomerative for 24 clusters without weights.
Data 09 00067 g006
Figure 7. Visualization for seven clusters and nine indicators multiplied with weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Figure 7. Visualization for seven clusters and nine indicators multiplied with weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Data 09 00067 g007
Figure 8. K-Means for seven clusters with weights.
Figure 8. K-Means for seven clusters with weights.
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Figure 9. Visualization for 24 clusters with weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Figure 9. Visualization for 24 clusters with weights. (a) K-Means, (b) GMM, (c) Agglomerative, and (d) Fuzzy C-Means.
Data 09 00067 g009
Figure 10. K-Means for 24 clusters with weights.
Figure 10. K-Means for 24 clusters with weights.
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Table 1. Size, focus, and age band categories’ definitions according to QS [2]. Reprinted with permission from Ref. [27].
Table 1. Size, focus, and age band categories’ definitions according to QS [2]. Reprinted with permission from Ref. [27].
SizeStudentsPerc. (%)
XLExtra LargeMore than 30,00023.8
LLarge≥12,00046.6
MMedium≥500023.8
SSmallFewer than 50005.8
FocusFaculty Area
FCFull Comprehensive5 Faculty Areas and Medical School41.6
COComprehensive5 Faculty Areas32.5
FOFocused3 or 4 Faculty Areas22.0
SPSpecialist2 or Fewer Faculty Areas3.9
ClassificationAge
5Historic100 Years Old and More37.5
4Mature50–99 Years Old34.4
3Established25–49 Years Old20.4
2Young10–24 Years Old6.7
1NewLess than 10 Years Old0.9
Table 2. Spearman rank correlation matrix for the nine indicators.
Table 2. Spearman rank correlation matrix for the nine indicators.
sizefocusagearerfsrcpfifrisr
size1
focus0.441
age0.220.211
ar0.340.390.331
er0.180.240.230.791
fsr−0.220.110.130.320.281
cpf0.110.260.20.510.320.071
ifr−0.0680.12−0.0030.450.420.210.451
isr−0.10.050.090.390.330.280.410.691
Table 3. Cluster sizes for seven clusters without weights.
Table 3. Cluster sizes for seven clusters without weights.
1234567
KM11291183126153332311
GMM44116618313097128163
Agg99138166183145220357
Fuzzy1382212141616281431
Table 4. Rand Index for seven clusters without weights.
Table 4. Rand Index for seven clusters without weights.
KMGMMAggFuzzyQS
KM1
GMM0.7295091
Agg0.866220.7360621
Fuzzy0.8311630.7232590.8196371
QS0.7767050.7607190.783350.7846931
Table 5. Differentiation between cluster members of Fuzzy C-Means and QS for seven clusters without weights.
Table 5. Differentiation between cluster members of Fuzzy C-Means and QS for seven clusters without weights.
Cluster1234567
CD151301331145677127
Table 6. Correlation of indicators and university cluster assignments for seven clusters without weights.
Table 6. Correlation of indicators and university cluster assignments for seven clusters without weights.
SizeFocusAgearerfsrcpfifrisr
Cluster−0.14−0.45−0.2−0.62−0.52−0.37−0.54−0.66−0.57
Table 7. Rand Index for 24 clusters without weights.
Table 7. Rand Index for 24 clusters without weights.
KMGMMAggFuzzyQS
KM1
GMM0.8951971
Agg0.9396020.8975481
Fuzzy0.8983270.8486780.8851551
QS0.902740.8720090.903250.8323281
Table 8. Cluster sizes for 24 clusters without weights.
Table 8. Cluster sizes for 24 clusters without weights.
123456789101112
KM283417212429703041622447
131415161718192021222324
423779603115437111701009169
GMM1182319196221111201323759
131415161718192021222324
3324165802842289173694977
Agg241956244865363027224437
131415161718192021222324
333247579588423112512013274
Fuzzy426249163559234109674130
131415161718192021222324
132212258112121251
Table 9. Differentiation between cluster members of Agglomerative and QS clusters for 24 clusters without weights.
Table 9. Differentiation between cluster members of Agglomerative and QS clusters for 24 clusters without weights.
Cluster123456789101112
CD81233224049332626214137
131415161718192021222324
31304354847537311069710065
Table 10. Rand Index for seven clusters with weights [27].
Table 10. Rand Index for seven clusters with weights [27].
KMGMMAggFuzzyQS
KM1
GMM0.756691
Agg0.8436520.7211431
Fuzzy0.8236690.7334210.7740221
QS0.7838110.7692520.7393590.77831
Table 11. Cluster sizes for seven clusters with weights.
Table 11. Cluster sizes for seven clusters with weights.
1234567
KM85152162166312202229
GMM30350136189162219222
Agg7713829117211422314
Fuzzy781382361931242420
Table 12. Differentiation between cluster members of K-Means and QS clusters for seven clusters with weights.
Table 12. Differentiation between cluster members of K-Means and QS clusters for seven clusters with weights.
Cluster1234567
CD95398107216144171
Table 13. Weights for the nine indicators. Reprinted with permission from Ref. [27].
Table 13. Weights for the nine indicators. Reprinted with permission from Ref. [27].
SizeAgeFocusarerfsrcpfifrisr
0.10.10.10.250.10.1250.1250.050.05
Table 14. Correlation of indicators and university cluster assignments for seven clusters with weights.
Table 14. Correlation of indicators and university cluster assignments for seven clusters with weights.
SizeFocusAgearerfsrcpfifrisr
Cluster−0.27−0.45−0.45−0.75−0.59−0.44−0.54−0.34−0.35
Table 15. Cluster sizes for 24 clusters with weights.
Table 15. Cluster sizes for 24 clusters with weights.
123456789101112
KM403828482323445335413356
131415161718192021222324
3468805953381338196389472
GMM178810140341361918433116
131415161718192021222324
1012539701472711435604014327
Agg38142544738562257354260
131415161718192021222324
82395813139644856601386887
Fuzzy282724394327388212198921
131415161718192021222324
76151223262122970000
Table 16. Differentiation between cluster members of K-Means and QS clusters for 24 clusters with weights.
Table 16. Differentiation between cluster members of K-Means and QS clusters for 24 clusters with weights.
Cluster123456789101112
CD41821301922323831332845
131415161718192021222324
3164665147371197269386763
Table 17. Rand Index for 24 clusters with weights.
Table 17. Rand Index for 24 clusters with weights.
KMGMMAggFuzzyQS
KM1
GMM0.9045051
Agg0.9389350.9078331
Fuzzy0.8799950.8473110.8731141
QS0.9160530.8825120.9086570.8456111
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Matlis, G.; Dimokas, N.; Karvelis, P. Unveiling University Groupings: A Clustering Analysis for Academic Rankings. Data 2024, 9, 67. https://doi.org/10.3390/data9050067

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Matlis G, Dimokas N, Karvelis P. Unveiling University Groupings: A Clustering Analysis for Academic Rankings. Data. 2024; 9(5):67. https://doi.org/10.3390/data9050067

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Matlis, George, Nikos Dimokas, and Petros Karvelis. 2024. "Unveiling University Groupings: A Clustering Analysis for Academic Rankings" Data 9, no. 5: 67. https://doi.org/10.3390/data9050067

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