How to make a scatter plot for clustering in Python?

A scatter plot for clustering visualizes data points grouped by cluster membership, with cluster centers marked distinctly. This helps analyze clustering algorithms like K-means by showing how data points are distributed across different clusters.

Basic Clustering Scatter Plot

Here's how to create a scatter plot that shows clustered data points with their centers ?

import numpy as np
import matplotlib.pyplot as plt

# Set figure size
plt.rcParams["figure.figsize"] = [8.00, 6.00]
plt.rcParams["figure.autolayout"] = True

# Generate sample data points
x = np.random.randn(15)
y = np.random.randn(15)

# Assign cluster labels (0, 1, 2, 3 for 4 clusters)
cluster_labels = np.array([0, 1, 1, 1, 3, 2, 2, 3, 0, 2, 0, 1, 3, 2, 1])

# Define cluster centers
centers = np.array([[0.5, 0.2], [-0.3, 0.8], [1.2, -0.5], [-1.0, -0.7]])

# Create the plot
fig = plt.figure()
ax = fig.add_subplot(111)

# Plot data points colored by cluster
scatter = ax.scatter(x, y, c=cluster_labels, s=80, alpha=0.7, cmap='viridis')

# Plot cluster centers
for center in centers:
    ax.scatter(center[0], center[1], s=200, c='red', marker='X', 
               edgecolors='black', linewidth=2, label='Center')

# Add labels and title
ax.set_xlabel('X coordinate')
ax.set_ylabel('Y coordinate')
ax.set_title('Scatter Plot for Clustering')
ax.grid(True, alpha=0.3)

# Show only one legend entry for centers
handles, labels = ax.get_legend_handles_labels()
if handles:
    ax.legend([handles[0]], ['Cluster Centers'])

plt.colorbar(scatter, label='Cluster ID')
plt.show()

Real-World Clustering Example

Here's a more realistic example using K-means clustering on generated data ?

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# Generate sample data with 3 natural clusters
data, true_labels = make_blobs(n_samples=150, centers=3, 
                              cluster_std=1.5, random_state=42)

# Apply K-means clustering
kmeans = KMeans(n_clusters=3, random_state=42)
predicted_labels = kmeans.fit_predict(data)
centers = kmeans.cluster_centers_

# Create the scatter plot
plt.figure(figsize=(10, 8))

# Plot original data points
plt.subplot(2, 1, 1)
plt.scatter(data[:, 0], data[:, 1], c=true_labels, cmap='viridis', alpha=0.7)
plt.title('True Clusters')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')

# Plot K-means results
plt.subplot(2, 1, 2)
plt.scatter(data[:, 0], data[:, 1], c=predicted_labels, cmap='viridis', alpha=0.7)
plt.scatter(centers[:, 0], centers[:, 1], c='red', marker='X', 
           s=200, edgecolors='black', linewidth=2, label='Centroids')
plt.title('K-means Clustering Results')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.legend()

plt.tight_layout()
plt.show()

Key Components

Essential elements of a clustering scatter plot include ?

• Data Points − Colored by cluster assignment using parameter c
• Cluster Centers − Marked with distinct symbols like 'X' or '+'
• Color Map − Different colors for each cluster using cmap
• Legend/Colorbar − Shows cluster identification

Customization Options

Conclusion

Scatter plots effectively visualize clustering results by color-coding data points and highlighting cluster centers. Use different markers and colors to distinguish between data points and centroids for clear cluster visualization.