## 4 Evaluation Study
### Results  
We ran a series of DBSCAN tests using the scikit-learn machine-learning library with a diverse range of parameters:  
* \(eps\in[0.1,3.0]\) with \(step=0.1\), which defines the maximum distance between two samples for one to be considered as in the neighborhood of the other. * \(min_{samples}\in[1,300]\) with \(step=1\), which defines the number of samples (i.e., total weight) in a neighborhood for a point to be considered as a core point (including the point itself). Regarding the analysis of the evaluation metrics, we produced the heatmaps depicted in Figure 5. Each heatmap reflects the assessment of each evaluation metric in a \(30\times 300\) matrix. Each row (\(N=30\)) represents the \(eps\) parameter, and each column (\(N=300\)) represents the \(min_{samples}\) parameter. The coloring follows a three-color scheme, with red indicating the poorest performance, yellow indicating the medium performance, and green indicating the best performance. Focusing on the evaluation parameters, this means that the greener a cell is, the higher the Silhouette coefficient is (i.e., close to 1), the higher the Calinski-Harabasz score is, and the lower the Davies-Bouldin index is (i.e., close to 0). Combining all, we achieved the best results for \(eps=1.1\) and \(min_{s}amples=19\), which provided \(N=7\) core clusters. We should note that the values of the selected parameters are close to the heuristic recommendations suggested by [PERSON] et al. [45]. Focusing on each metric, we had \(s=0.792\) as the Silhouette coefficient, which is close to the maximum value (i.e., 1), meaning that the core clusters are characterized by high density, there are no overlapping core clusters, they are well apart from each other, and they are clearly distinguished. This is also confirmed by the Calinski-Harabasz score, which indicates, with a maximum value of \(s=2350\), that the core clusters are dense and well separated, relating to a standard concept of a cluster. We should also mention that there is a high degree of correlation between these two metrics and that our results are in line with results of studies in other domains (e.g., [46]), which indicates a high volume of consistency and increased validity. Regarding the Davies-Bouldin index (minimum value: \(DB=0.348\)), the analysis indicates that the clustering model performed well. This is attributed to the fact that the clusters are not similar to each other, and thus, the best clustering scheme minimizes the Davies-Bouldin index. We should highlight that the evaluation of CAVeCTIR is based on several parameters, including the DBSCAN algorithm, the Silhouette coefficient, the Calinski-Harabasz score, and the Davies-Bouldin index. They all have advantages and disadvantages. DBSCAN performs well for datasets with noise, recognizes outliers easily, and the clusters can develop irregular shapes. However, it is sensitive to the _eps_ and _minsamples_ parameters, which justifies our decision to explore a variety of combinations. The Silhouette coefficient provides an easy metric for defining the quality of the clustering, but it normally needs high computational effort. The Calinski-Harabasz score tends to be most suitable when the identified clusters are more or less spherical and compact in their middle (e.g., normal distribution). It also tends to have high scores when the clusters have similar sizes. Regarding the Davies-Bouldin index, it has simpler and quicker computation than the Silhouette coefficient, and it is computed based on quantities and features inherent to the available data. However, it is generally higher for convex clusters, it limits the distance metric to the Euclidean space, and a good index value does not always guarantee the best results regarding information retrieval. For all of the above, we decided to use these metrics as complementary to take the best from their application in a real-life IoV scenario.