Density-based spatial clustering of applications with noise (DBSCAN) is about classifying the data points in the dataset as core data points, border data points, and noise data points, with the use of density relations between points, such as directly density-reachable, density-reachable, and density-connected points. The major characteristics of DBSCAN are as follows:
- No need to specify the number of clusters to be generated
- Good at dealing with large datasets having noise
- Dealing with clusters of various shapes
In the following example, we will show you how to use DBSCAN to perform density-based clustering.