## 4 Document Visualization
### Document Embeddings
#### 4.1.4 Clusters' Quality Analysis  
To assess the quality of the hand-picked clusters, we conducted an experiment using cosine similarity. We utilized the preprocessing pipeline outlined above, and then, we compared the cosine similarity of each document to any other in the data set, excluding self-comparisons to maintain fairness in the evaluation. The obtained results are presented in Table 8. \begin{table}
\begin{tabular}{l c} \hline \hline
**Name of the Cluster** & **Number of Articles** \\ \hline Artificial Intelligence (AI) & 20 \\ Astrophysics Galaxies (APG) & 20 \\ Bicycle Sharing Systems (BSS) & 19 \\ Computer Graphics—Ambient Occlusion (AO) & 24 \\ Electrical Engineering 2022 (EE22) & 22 \\ Electrical Engineering 2015 (EE15) & 24 \\ Global Illumination (GI) & 25 \\ High-Energy Astrophysics (HEAP) & 20 \\ Information Theory (IT) & 23 \\ Molecular Visualization in Virtual Reality (MVVR) & 20 \\ Viewpoint Selection (VS) & 19 \\ Visualization (Vis) & 20 \\ Volume Rendering (VolRend) & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Contents of the different clusters. Considering the documents of each class as a cluster, we observed that we achieved higher average cosine similarity (coherency) within documents of the same cluster compared to the other clusters. Additionally, we observed that some clusters were more coherent than others. For instance, the Electrical Engineering 2022 cluster was more similar to the Artificial Intelligence (0.104) than the Electrical Engineering 2015 one (0.091). This aligned with our earlier observation that many recent Electrical Engineering articles often incorporate deep learning techniques. Furthermore, there was a notable cosine similarity between some clusters such as Higher Energy Astrophysics and Astrophysics Galaxies (0.248 self and 0.249 for AG) as well as dissimilarity in some others, namely Ambient Occlusion (0.39 for self) and Electrical Engineering (0.095, 0.011 in 2015 and 2022 respectively). These findings were consistent with our expectations. Another result that confirmed the expected behavior of our approach was the similarity between related areas, such as Global Illumination and Ambient Occlusion, both of which addressed a similar goal (realistic image synthesis), through different techniques. Molecular Visualization in Virtual Reality, which extensively employed Computer Graphics techniques, exhibited a higher similarity to the Computer Graphics--Ambient Occlusion cluster. Similarly, the Volume Rendering had the Virtual Reality cluster as a closer one. When applying the PaCMAP for dimensionality reduction on the mentioned data, it was evident from Figure 8a that not all areas were adequately clustered into distinct and visible groups. Certain clusters appeared to be tightly packed together, and the algorithm encountered difficulties in effectively segregating them. Nonetheless, it is noteworthy that the clustering pattern was in accordance with our previous observations. Astrophysics clusters were mixed at the bottom, while certain groups, like Information Theory and Bicycle Sharing Systems, were distinctly positioned away from the central group of Computer Graphics-related clusters. Despite these similarities, the algorithm still failed to create more well-defined clusters. However, with the application of the improved version of the algorithm, shown in Figure 8b, significant improvements were evident. The enhanced version effectively divided the clusters, such as the two Astrophysics groups. Furthermore, the Electrical Engineering groups were now distinct from the others. A clear cluster for Viewpoint Selection was also observable. This demonstrates the positive impact of our modifications