Taller Clustering datos ficticios

Aplicar los métodos de clustering aprendidos a cada uno de los siguientes conjuntos de datos y determinar visualmente la mejor combinación de clusters.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs, make_moons, make_circles

# Generar diferentes formas de datos
def generate_datasets():
    n_samples = 1500
    noisy_circles = make_circles(n_samples=n_samples, factor=.5, noise=.05)
    noisy_moons = make_moons(n_samples=n_samples, noise=.05)
    blobs = make_blobs(n_samples=n_samples, random_state=8)
    no_structure = np.random.rand(n_samples, 2), None

    # Anisotropicly distributed data
    random_state = 170
    X, y = make_blobs(n_samples=n_samples, random_state=random_state)
    transformation = [[0.6, -0.6], [-0.4, 0.8]]
    X_aniso = np.dot(X, transformation)

    # Varying standard deviation of blobs
    varied = make_blobs(n_samples=n_samples,
                        cluster_std=[1.0, 2.5, 0.5],
                        random_state=random_state)

    return [noisy_circles, noisy_moons, blobs, no_structure, (X_aniso, y), varied]

datasets = generate_datasets()

# Definir una función para visualizar los datos
def plot_datasets(datasets):
    plt.figure(figsize=(12, 8))
    for i, (X, y) in enumerate(datasets):
        plt.subplot(2, 3, i + 1)
        plt.scatter(X[:, 0], X[:, 1], s=10)
        plt.title(f'Dataset {i + 1}')
        plt.xticks(())
        plt.yticks(())
    plt.tight_layout()
    plt.show()

# Visualizar los datasets generados
plot_datasets(datasets)