Choosing the right clustering algorithm
Selecting the most suitable clustering algorithm depends heavily on the structure and properties of the dataset. There’s no one-size-fits-all solution – different algorithms are suited to different types of data distributions, levels of noise, and dimensionality! This recipe compares key characteristics of clustering algorithms and provides guidance for choosing among them.
Getting ready
Let’s begin by creating a variety of dummy datasets using scikit-learn functions we’ve used before.
- Load the libraries:
from sklearn.datasets import ( make_moons, make_blobs, make_ circles) from sklearn.preprocessing import StandardScaler
- Create and scale different datasets:
X_blobs, _ = make_blobs( n_samples=300, centers=3, cluster_std=0.6, random_state=2024 ) X_moons, _ = make_moons( n_samples=300, noise=0.1, ...
