An example of K-Means++ initialization

An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating initial seeds for clustering.

K-Means++ is used as the default initialization for K-means.

K-Means++ Initialization
print(__doc__)

from sklearn.cluster import kmeans_plusplus
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

# Generate sample data
n_samples = 4000
n_components = 4

X, y_true = make_blobs(n_samples=n_samples,
                       centers=n_components,
                       cluster_std=0.60,
                       random_state=0)
X = X[:, ::-1]

# Calculate seeds from kmeans++
centers_init, indices = kmeans_plusplus(X, n_clusters=4,
                                        random_state=0)

# Plot init seeds along side sample data
plt.figure(1)
colors = ['#4EACC5', '#FF9C34', '#4E9A06', 'm']

for k, col in enumerate(colors):
    cluster_data = y_true == k
    plt.scatter(X[cluster_data, 0], X[cluster_data, 1],
                c=col, marker='.', s=10)

plt.scatter(centers_init[:, 0], centers_init[:, 1], c='b', s=50)
plt.title("K-Means++ Initialization")
plt.xticks([])
plt.yticks([])
plt.show()

Total running time of the script: ( 0 minutes 0.101 seconds)

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