In order for DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to function, data points are grouped according to how dense they are in the feature space. Core points are found by the method when they have a given number of surrounding points within a given distance (ε). Furthermore, locations that fall under the density criteria but are not next to core points are designated as border points. Clusters are created when the algorithm iteratively grows from core points to boundary points inside a given density-connected area. Noise is the term applied to unassigned points. Since the number of clusters is not fixed, DBSCAN can find clusters of different sizes and forms.
Clusters labeled as 4 and 2 in DBSCAN would be a result of the algorithm’s exploration of the data’s density distribution and spatial relationships between points, with each cluster characterized by its unique density-connected region.