clusterização com K-Means + visualização gráfica em Python

import numpy as np

import matplotlib.pyplot as plt

from sklearn.datasets import make_blobs

from sklearn.cluster import KMeans

X, y_true = make_blobs(n_samples=300, centers=2, cluster_std=1.5, random_state=0)

kmeans = KMeans(n_clusters=2, n_init='auto')

kmeans.fit(X)

y_kmeans = kmeans.predict(X)

centers = kmeans.cluster_centers_

x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1

y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),

                     np.arange(y_min, y_max, 0.02))

Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])

Z = Z.reshape(xx.shape)

plt.figure(figsize=(10, 6))

plt.contour(xx, yy, Z, colors='red', linewidths=2, linestyles='--')

plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis', alpha=0.7, edgecolors='k')

#plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, marker='X')

#plt.xlabel('Coordenada X')

#plt.ylabel('Coordenada Y')

plt.legend()

plt.grid(True, linestyle='--', alpha=0.5)

plt.show() 

Saída gerada