## 4 Experiments and Results
### Analysis Results for Suburban Area
#### 4.2.2 Weekday Suburban Analysis  
Figure 5 shows the clustering validity indices curves derived from the four explored metrics using the STP data analysis from the suburban environment during the weekday. It can be observed that the solutions of \(Q=3\) (for Gap and Calinsky-Harabasz) and \(Q=5\) (for Davies-Bould and Silhouette) clusters attained local optimum values, for the Silhouette and Gap indices being the number of clusters that attained a significant increase of the validity index within the lower number of clusters. Figure 6 shows the resulting dendrogram of the agglomerative hierarchical clustering for the suburban environment on the weekday, where the solutions for \(Q=3\) and \(Q=5\) are also highlighted. Figure 7 shows the obtained similarity matrix \(\mathbf{S}\). As can be seen, the solution is compliant with the clustering solution \(Q=3\) (shown in red rectangles on the left-side), as well as \(Q=5\) (shown with white dashed lines across the image, but also as green rhomboids at the bottom of the figure). In the figure, and considering the clustering solution for \(Q=5\), the inter (for \(i=j\)) and intra-cluster (for \(i\
eq j\)) Euclidean distances are also shown as \(D_{ij}\) (note that indices \(i\) and \(j\) refer to the numeration of clusters of the \(Q=5\) solution shown in Figure 6, e.g., \(Ci\) in green). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **Environment** & **Sensed Day** & **Criterion** & **Discarded 1-h** \\  & & & & **Periods** \\ \hline hb104 & Suburban & Weekend & Rain & 13:00, 15:00 \\ hb105 & Suburban & Weekend & Rain & 15:00 \\ hb112 & Suburban & Weekend & \% RTN \(<\gamma\) & 15:00 \\ hb119 & Suburban & Both & Tech. problem & All \\ hb134 & Suburban & Weekend & \% RTN \(<\gamma\) & 15:00 \\ hb141 & Suburban & Weekend & Rain & 15:00 \\ hb143 & Suburban & Weekend & \% RTN \(<\gamma\) & 11:00 \\ hb147 & Suburban & Weekend & Rain & 15:00 \\ hb148 & Suburban & Weekend & Rain & 15:00 \\ hb149 & Suburban & Weekend & Rain & 15:00 \\ hb153 & Suburban & Weekend & Rain & 13:00, 15:00 \\ hb154 & Suburban & Weekend & \% RTN \(<\gamma\) & 13:00, 15:00 \\ \hline hb114 & Urban & Both & Tech. problem & All \\ hb117 & Urban & Weekday & \% RTN \(<\gamma\) & 8:00, 14:00 \\ hb124 & Urban & Weekday & \% RTN \(<\gamma\) & 15:00 \\ hb125 & Urban & Weekday & \% RTN \(<\gamma\) & 9:00 \\ hb133 & Urban & Weekday & \% RTN \(<\gamma\) & 03:00, 17:00 \\ hb133 & Urban & Weekend & \% RTN \(<\gamma\) & 23:00 \\ hb137 & Urban & Weekday & \% RTN \(<\gamma\) & 09:00 \\ hb138 & Urban & Weekday & \% RTN \(<\gamma\) & 11:00 \\ hb145 & Urban & Weekend & \% RTN \(<\gamma\) & 14:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Discarded 1-h periods from both suburban and urban environments. Figure 4: Examples of original (left-side) and interpolated (right-side) STPs for the suburban environment from (**a**) sensor hb140 and (**b**) sensor hb143, respectively. Figure 5: Clustering validity indices obtained for the acoustic suburban environment on the weekday, 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. As an overall picture, Figure 7 enables us to see clearly that the \(Q=5\) clusters obtained from the STPs exhibited different patterns, with the inner similitude between sensors of each cluster being very high, because the distances in \(D_{11},D_{22},D_{33},D_{44}\) and \(D_{55}\) regions were somewhat lower than the others. Due to the hierarchical nature of the clustering technique, the solution for \(Q=5\) also included the \(Q=3\) solution, where two clusters of \(Q=3\) also included two other clusters of \(Q=5\) (see Figure 6).