Advanced Clustering Techniques

Beyond the classic clustering algorithms like, scikit-learn offers several advanced techniques such as Spectral Clustering and Gaussian Mixture Models (GMMs). These methods provide more flexibility in modeling complex cluster shapes and probabilistic cluster assignments, making them useful for more nuanced tasks. This recipe will provide the basic guidelines for generating models with these two techniques.

Getting ready

Let’s begin by loading our two new clustering models and generating some dummy data to test them on.

1. Load the libraries:

   from sklearn.cluster import SpectralClustering
   from sklearn.mixture import GaussianMixture

2. Create datasets with complex structure:

   from sklearn.datasets import make_moons
   from sklearn.preprocessing import StandardScaler
   X, _ = make_moons(n_samples=300, noise=0.05, random_state=2024)
   X = StandardScaler().fit_transform(X)

now let’s apply each technique to the generated data.

How to do it...

Each model is...