# Analysis and Evaluation of Clustering Techniques Applied to Wireless Acoustics Sensor Network Data
## 3 Results and Discussion  
In this section, the results of the performance of the different unsupervised learning algorithms are shown to evaluate and compare them with the three metrics explained in Section 2.3. Moreover, a selection of the best clustering algorithm to work with acoustic data to identify behavior patterns is completed. Additionally, a more detailed discussion of the resulting clustering is presented. This discussion is carried out by comparing cluster outputs from both federated data, that is, dataset containing data from both cities, and non-federated data, that is, dataset containing data from only one city. For each normalized dataset, clustering algorithms listed in Section 2.2 are trained several times, increasing the number of clusters from 3 to 12, to fit the three different datasets presented in Section 2. For the interest of the research, the case \(k=2\) is avoided because it uses to separate the nodes in one group of high sound pressure level values \begin{table}
\begin{tabular}{c c} \hline \hline
**Software Environment** & **Version** \\ \hline On-Premise & R version 4.1.0 called “Camp Pontanezen” \\ AMD Ryzen 7 3700X 8-Core Processor 3.60 GHz with 16 GB RAM \\ and a GTX 1660 Super GDDR5 GPU. \\ \hline Cloud & R version 4.2.0 called “Vigorous Calisthenics” \\  & RStudio Cloud Server \\ \hline
**Library** & **Version** \\ \hline stringr & 1.4.0 \\ dplyr & 1.0.5 \\ tidyr & 1.1.3 \\ cluster & 2.1.1 \\ ggplot2 & 3.3.3 \\ clValid & 0.7 \\ mclust & 5.4.8 \\ kohonen & 3.0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Libraries and Software Versionsand another of low sound pressure level values, not adding value since that is what city managers usually do. This particular case has been enunciated in previous literature [16; 27] that would not help to discover new knowledge. Firstly, Table 3 shows the results for the Connectivity metric of the different techniques. A first comparison of the resulting values offers that the best algorithm for the Connectivity metric, the optimum algorithms are Hierarchical Agglomeration and K-means. Note that values are highlighted in Table 3. From these results an important insight could be extrapolated, that the number of optimal clusters for the Connectivity metric holds in three. Now, in Table 4 the Dunn index obtained for all the algorithms and 3 to 12 clusters are shown. It is observed that this metric aims to create a higher amount of clusters, prioritizing separation from compactness. Again, note that the highest values are highlighted. For the Dunn index, Hierarchical Agglomeration and K-means algorithms are also the top performers. Finally, Table 5 shows the Silhouette Width for all the clustering techniques and for the same number of clusters and the datasets previous indicated. Hierarchical Agglomeration and K-means algorithms also maximize the Silhouette Width. For this metric, it is shown in Table 5 that the Barcelona dataset and the federated dataset are recommended to be split into 4 clusters, but for the Madrid dataset, the recom  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{6}{c}{**Number of Clusters**} & \\
**Algor.