## 4 Experimental Results
### Generative Art Gallery
This gave the Pearson correlation matrix in Figure 11C. Figure 11A shows the two trajectories with the highest Pearson correlations, \(\rho(a,b)=0.83\). Figure 11B shows the two trajectories with the lowest Pearson correlations, \(\rho(a,b)=-0.12\). To compute the WCD between two clusters, we used the complete condition in Equation (2) with \(d(a,b)=1-\rho(a,b)\). Given these assumptions, we ran the agglomerative clustering algorithm and obtained the dendrogram in Figure 11C. Figure 10: (**1**) The art gallery scene; (**2**) a comparison of three different sessions from the Paestum event with zig-zag patterns and trajectory knots; (**3**) an immersive inspection of the KDE computed on users’ locations; (**4**) an inspection of a complex record annotated by analysts and focal fixations; (**5**) remote analysts discussing fixations with synchronized time. Figure 11: Agglomerative clustering of generative art gallery users’ trajectories. Trajectories are sequences of users’ positions \(P\) recorded with a 0.2 s sample time. (**A**) shows the two trajectories with the highest correlation (best match). (**B**) shows the two trajectories with the lowest correlation (poorest match). The color of the marker indicates the density value estimated by the KDE on the single trajectory at this point, and it allows us to visualize how much time the user spent in this area. (**C**) Agglomerative clustering dendrogram, obtained using \(d(x,y)=1-corr(x,y)\) as a metric (where \(corr(x,y)\) is the Pearson correlation between the KDEs of two trajectories), and the complete linking condition. The y-axis is the WCD, the linking condition value at which the clusters merge. We obtained seven clusters by stopping the agglomerative clustering at the WCD = 0.87. To the right of the dendrogram, we present the Pearson correlation matrix between all trajectory KDE couples. (**D**) The user trajectories are divided into seven clusters labeled from 1 to 7. The last column shows the 2D KDE obtained from the agglomeration of the trajectories in each cluster. If we stop the clustering process at WCD under 0.87, we find seven clusters. Cluster 1 is composed of trajectories of users that mostly stopped in front of the hand and Dalli-like artwork, very close to the wall. By inspecting the single users' trajectories with the inspection service, we found that this group mostly focused on the hand and the Dali-like artwork. The users also focused on the crying Statue of Liberty and the generated video from a distance, without moving closer. The users in Cluster 2 stopped in front of most of the artwork. These users tended to position themselves further from the artwork, obtaining a more comfortable view. By inspecting the single users' trajectories with the inspection service, we found that these users observed the artwork more systematically, observing all pieces evenly. Cluster 3 mostly stopped slightly above the center and used the teleport less frequently. By inspecting the single users' trajectories with the inspection service, we noticed that they mostly observed the hand and the Dali-like artwork together with the crying Statue of Liberty. Cluster 4 spent more time in front of the sunset artwork. This group tended to observe the Dali-like artwork and the crying Statue of Liberty as in the other groups but spent more time observing the sunset artwork on the left side of the room. Cluster 5 is the second largest cluster, and the users in this cluster spent more time on the side in front of the \"Carnival of the Ages\". This group tended to observe the Dali-like artwork but, in comparison to the others, spent more time observing the \"Carnival of the Ages\" video and the crying Statue of Liberty. Cluster 6 is composed of only two records; the users of this cluster positioned themselves in the center of the room for most of the time and observed most of the artwork from there. Cluster 7 comprises users who used the teleport less frequently and mostly stopped in positions near the center.