## 4 Results
Now, after having all the supervised classification predictions, we ran the clustering approach with the k-means and k-medoids algorithms with three clusters to try to identify our three classes, which corresponded to home, work, and forest, evaluating the performance of these methods compared to supervised learning. Table 5 presents the accuracy obtained from the clustering algorithms, kMedoids and kMeans. We can already see that the results are poor compared to the supervised approach, in which RF obtained 0.647 accuracy. We now show in Figure 7 the kMeans clustering predictions for the tested subgrid. It shows the Internet results with three clusters, as it provided the highest accuracy for the clustering tests. This prediction failed to detect our labels of home, work, and forest and instead provided something resembling more of the density of the province: while the city center of Trento is one cluster, the surroundings are the second cluster, and the rest with small villages and forest being the last cluster. Since RF performed best for the 15 \(\times\) 15 and the 4 \(\times\) 4 forest subgrids, we then predicted the complete Trentino grid in order to evaluate the goodness of the method for generalization. Figure 8 shows the predictions for the complete grid. The grids in green indicate the absence of data for those grids since the area covered in the province of Trento has very isolated and alpine areas for which no records were generated in the CDRs. Again, home is represented in blue, work is in yellow, and forest is in red. Here, we can see that the forest areas were clearly visible and well-identified. The home label is also represented \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **smsin** & **smsout** & **Callin** & **Callout** & **Internet** \\ \hline KMedoids 3 clusters, Manhattan & 0.3858 & 0.2406 & 0.3941 & 0.3402 & 0.3900 \\ KMedoids 3 clusters, CID & 0.3568 & 0.3319 & 0.3568 & 0.4024 & 0.2946 \\ KMedoids 3 clusters, Euclidean & 0.3278 & 0.2987 & 0.2282 & 0.3651 & 0.2282 \\ KMeans 3 clusters & 0.1037 & 0.1078 & 0.1120 & 0.1120 & 0.4190 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Clustering accuracy. Figure 6: (**a**) Ground truth and (**b**) classification predictions for the 15 \(\times\) 15 subgrid classified with DL for smsout. Yellow: work, blue: home, red: forest. Geojson.io visualization. well through the map, as it detected most of the rural villages, communes, and towns from Trentino, such as Arco, Rovereto, Cavalese, or Cles. For work areas, it mostly detected the biggest ones from the city of Trento, and the one from Pergine Valsugana. We also calculated the predictions of kMeans for the full grid as a way to show the differences between the unsupervised and supervised approach. Figure 9 shows the kMeans clustering predictions for the whole grid. Figure 8: RF classification (500 estimators) for the full grid. Green: no data present, yellow: work, blue: home, red: forest. Geojson.io visualization. Figure 7: (**a**) Ground truth and (**b**) classification predictions for the 15 \(\times\) 15 subgrid classified with kMeans clustering for internet. Yellow: work, blue: home, red: forest. Geojson.io visualization. We immediately see the difference in clustering in this case because it mostly classified all the province of Trento as one cluster, matching with a low-density area, like the forest and mountainous areas, but failed to detect communes or small villages from around the province. It only marked the city of Trento and some cells from Mezzolombardo, Riva del Garda, Mori, and Rovereto as home areas. Meanwhile, the random forest method made a good work of generalizing the training sample and provided a much better overview of the province of Trento and the labels that we intended to detect.