## 4 Experiments and Results
### Analysis Results for Suburban Area
#### 4.2.3 Weekend Suburban Analysis  
An equivalent clustering and analysis was conducted for the weekend, considering the corresponding STPs. First, the clustering validity indices curves are shown in Figure 8. In this case, \(Q=2\) (where Silhouette and Calinski-Harabasz indices attained local maxima) as well as \(Q=4\) (where Davies-Bouldin attained a local minimum) are the main interesting values to be considered as optimal number of clusters. The dendrogram of the agglomerative hierarchical clustering is depicted in Figure 9. Compared to the clustering solutions analysed for the weekday, it can be seen that \(Q=5\) on the weekday and \(Q=4\) during the weekend shared some similarities: (i) sensors hb111 and hb156 are grouped together; (ii) sensors hb104 and hb153 form a group on the weekday, and together with their isolate cluster with hb155 to form a new cluster at the weekend. However, the rest of the sensor locations are grouped as a whole in the \(Q=4\) solution for the weekend, while they are separated in two different clusters for the \(Q=5\) solution on the weekday.  
Figure 6: Dendrogram of the agglomerative hierarchical clustering for the suburban environment on the weekday. Clustering solutions for \(Q=3\) (in red) and \(Q=5\) (in green) are also highlighted.  
Figure 8: Clustering validity indices obtained for the acoustic suburban environment at the weekend, resulting from the agglomerative hierarchical clustering algorithm for the considered number of clusters sweep \(2\leq Q\leq 18\). The suboptimal number of clusters are marked as red asterisks, being the local maxima in all clustering indices despite the Davies–Bouldin index, which is a local minimum.  
Figure 7: Similarity matrix (**S**) for the suburban environment on the weekday. White dashed lines separate the \(Q=5\) clustering solution, which in turn also includes the optimal \(Q=2\) solution. \(Q=5\) clusters are also highlighted at the bottom by means of green rhomboids and \(Q=2\) clusters on the left-side with red rectangles. Inter (\(i=j\)) and intra-cluster (\(i\
eq j\)) distances are highlighted in white and denoted as \(D_{ij}\).  
Figure 10 shows the similarity matrix \(\mathbf{S}\) for the suburban environment. Compared with the same matrix computed for the weekday, it can be seen that there are many similarities between both results. Looking at the differences, it is worth noting the higher distances between sensors hb134 and hb154 with respect to the most similar cluster formed by sensors hb110, hb141, hb103, hb148, and hb128 for the weekend analysis compared to its weekday counterpart (see Figure 7).  
Figure 10: Similarity matrix (\(\mathbf{S}\)) for the suburban environment at the weekend. White dashed lines separate the four clusters of the \(Q=4\) clustering solution (marked as green rhomboids at the bottom), which, in turn, also includes the \(Q=2\) solution (also marked on the left-side as red rectangles). Inter-(\(i=j\)) and intra-cluster (\(i\
eq j\)) distances are highlighted in white and denoted as \(D_{ij}\).  
Figure 9: Dendrogram of the agglomerative hierarchical clustering for the suburban environment at the weekend. Clustering solutions for \(Q=2\) (in red) and \(Q=4\) (in green) are also highlighted.