Let's consider a more complex dummy dataset with 8 centers:
from sklearn.datasets import make_blobsnb_samples = 3000X, Y = make_blobs(n_samples=nb_samples, n_features=2, centers=8, cluster_std=2.0)
A graphical representation is shown in the following graph:
Dataset created for Agglomerative Clustering examples
We can now perform Agglomerative Clustering with different linkages (always keeping the Euclidean distance) and compare the results. Let's start with a complete linkage (AgglomerativeClustering uses the fit_predict(); method to train the model and transform the original dataset), also computing ...
