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.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt

from sklearn.cluster import kmeans_plusplus
from sklearn.datasets import make_blobs

# 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 k-means++
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 0.062 seconds)

Download Jupyter notebook: plot_kmeans_plusplus.ipynb

Download Python source code: plot_kmeans_plusplus.py

Download zipped: plot_kmeans_plusplus.zip

Related examples

GMM Initialization Methods

Bisecting K-Means and Regular K-Means Performance Comparison

A demo of K-Means clustering on the handwritten digits data

Comparison of the K-Means and MiniBatchKMeans clustering algorithms