Density-Based Clustering with DBSCAN

DBSCAN is a somewhat unique clustering algorithm capable of identifying clusters of varying shapes and sizes. It differs from K-means and hierarchical clustering by not requiring the number of clusters to be specified in advance and by handling outliers (noise) effectively. This means, unlike K-means, it does not generate centroids a priori. This recipe applies DBSCAN on a rather unconventional dataset.

Getting ready

Here, we are going to use another data generator function in scikit-learn called make_moons(), which, again, like make_blobs() is aptly named!

1. Load the libraries:

   from sklearn.cluster import DBSCAN
   from sklearn.datasets import make_moons

2. Create a new one with noise:

   X, _ = make_moons(n_samples=300, noise=0.1, random_state=2024)

Another scikit-learn dataset generator (make_moons) creates crescent shaped data sets for exploring clustering algorithms that work better with data not arranged in spherical groupings

How to do it...

Let&...