## 4 Document Visualization
### Document Embeddings
#### 4.1.4 Clusters' Quality Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & **AI** & **APG** & **BSS** & **AO** & **EE15** & **EE22** & **GI** & **HEAP** & **IT** & **MVVR** & **VS** & **Vis** & **VolRend** \\ \hline AI & **0.144** & 0.102 & 0.130 & 0.129 & 0.091 & 0.104 & 0.116 & 0.096 & 0.129 & 0.121 & 0.125 & 0.114 & 0.123 \\ APG & 0.102 & **0.290** & 0.213 & 0.195 & 0.077 & 0.093 & 0.106 & 0.249 & 0.189 & 0.200 & 0.192 & 0.099 & 0.190 \\ BSS & 0.130 & 0.213 & **0.374** & 0.214 & 0.106 & 0.111 & 0.128 & 0.201 & 0.223 & 0.234 & 0.231 & 0.162 & 0.217 \\ AO & 0.129 & 0.195 & 0.214 & **0.397** & 0.095 & 0.111 & 0.250 & 0.186 & 0.213 & 0.239 & 0.286 & 0.161 & 0.346 \\ EE15 & 0.091 & 0.077 & 0.106 & 0.095 & **0.115** & 0.092 & 0.080 & 0.079 & 0.097 & 0.091 & 0.099 & 0.080 & 0.098 \\ EE22 & 0.104 & 0.093 & 0.111 & 0.111 & 0.092 & **0.117** & 0.105 & 0.088 & 0.109 & 0.102 & 0.110 & 0.093 & 0.116 \\ GI & 0.116 & 0.106 & 0.128 & 0.250 & 0.080 & 0.105 & **0.227** & 0.106 & 0.121 & 0.148 & 0.178 & 0.137 & 0.221 \\ HEAP & 0.096 & **0.249** & 0.201 & 0.186 & 0.079 & 0.088 & 0.106 & **0.248** & 0.181 & 0.185 & 0.180 & 0.099 & 0.185 \\ IT & 0.129 & 0.189 & 0.223 & 0.213 & 0.097 & 0.109 & 0.121 & 0.181 & **0.312** & 0.208 & 0.231 & 0.123 & 0.215 \\ MVVR & 0.121 & 0.200 & 0.234 & 0.239 & 0.091 & 0.102 & 0.148 & 0.185 & 0.208 & **0.313** & 0.232 & 0.161 & 0.241 \\ VS & 0.124 & 0.195 & 0.232 & 0.270 & 0.096 & 0.107 & 0.168 & 0.181 & 0.223 & 0.259 & **0.301** & 0.163 & 0.274 \\ Vis & 0.115 & 0.099 & 0.163 & 0.160 & 0.080 & 0.093 & 0.136 & 0.099 & 0.121 & 0.158 & 0.163 & **0.194** & 0.174 \\ VolRend & 0.123 & 0.190 & 0.217 & 0.346 & 0.098 & 0.116 & 0.221 & 0.185 & 0.215 & 0.241 & 0.291 & 0.176 & **0.347** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The average similarity scores for the different clusters in the technical documents’ dataset. Notice how the inner average similarity of each class is consistently lower than the average similarities against other classes, except for the HEAP (High-Energy Astrophysical Phenomena) and APG (Astrophysics of Galaxies) classes. This aligns with the fact that both classes are closely related and belong to the same arXiv superclass. Additionally, as anticipated, the Volume Rendering and Ambient Occlusion classes exhibit a high degree of similarity. In addition, we can also see that the Volume Rendering and Ambient Occlusion are very similar, as expected. on the algorithm's performance, particularly when dealing with a challenging dataset that includes papers from the Computer Graphics field and closely related areas. In addition, the improved results are also observed in the case of other DR algorithms, as depicted in Figure 8d,f,h. For instance, the tSNE algorithm exhibited more sparse clusters, which could potentially be further optimized with different numbers of neighbors. Nonetheless, as previously mentioned, we retained the default parameters of the DR algorithms to prevent any unfair fine-tuning. In the second experiment, we present the results from a new dataset that differed from the carefully curated one used previously. Instead of a deliberately designed dataset, this collection was constructed by selecting the last 21 articles available arXiv database on different areas. The dataset comprised six categories: Astrophysics, Computer Science, Electrical Engineering, Mathematics, Quantum Physics, and Statistics. For each category, the last 21 available articles were downloaded. The documents from this dataset underwent the same preprocessing pipeline as described earlier. Subsequently, we conducted similar experiments to evaluate clustering performance. The results obtained from these experiments are illustrated in Figure 9. In contrast to the previous dataset, here, the DR algorithms demonstrated less success in identifying clusters. These results were enhanced through our data manipulation, leading to the increased visibility of certain clusters, for example with PaCMAP (b). The modified vectors exhibited improved performance with other DR algorithms as well.