Synthetic Walkthrough

from sklearn.datasets import make_blobs
from core_sg import CoreSGClusterer

X, _ = make_blobs(n_samples=1000, n_features=10, centers=5, random_state=42)

clusterer = CoreSGClusterer(k_max=20, metric="euclidean", p=2)
clusterer.fit(X, k=10)

print(clusterer.labels_)

This example builds one reusable internal support graph and extracts one hierarchy through the estimator.