Creating clusters using DBSCAN in Python
First, we will import the necessary functions from the sklearn library:
from sklearn.cluster import DBSCAN
from sklearn.datasets import make_moons
Let’s employ DBSCAN to tackle a slightly more complex clustering problem, one that involves structures known as “half-moons.” In this context, “half-moons” refer to two sets of data points that are shaped like crescents, with each moon representing a unique cluster. Such datasets pose a challenge because the clusters are not linearly separable, meaning a straight line cannot easily divide the different groups.
This is where the concept of “nonlinear class boundaries” comes into play. In contrast to linear class boundaries, which can be represented by a straight line, nonlinear class boundaries are more complex, often necessitating curved lines or multidimensional surfaces to accurately segregate different classes or clusters.
To generate...