K-means clustering is an unsupervised machine learning algorithm used to group data points into k clusters based on similarity. Here's a quick guide to understanding its core concepts and implementation!

📘 What is K-Means Clustering?

K-means aims to partition data into k distinct groups by minimizing the distance between points within the same cluster. Key steps include:

  1. Initialize k cluster centers randomly
  2. Assign data points to the nearest center
  3. Recalculate centers based on assigned points
  4. Repeat until convergence

📊 Visual Example:

k_means_clustering

✅ How to Implement K-Means

Here’s a simple Python example using scikit-learn:

from sklearn.cluster import KMeans
import numpy as np

# Sample data
data = np.array([[1,2], [1,4], [1,0], [4,2], [4,4], [4,0]])

# Initialize model
kmeans = KMeans(n_clusters=2, random_state=0)

# Fit model
kmeans.fit(data)

# Predict clusters
labels = kmeans.predict(data)
print("Cluster labels:", labels)

🔍 Visualize Process:

iteration_process

🌍 Applications of K-Means

  • Customer segmentation in marketing
  • Image compression
  • Anomaly detection
  • Document clustering

📊 Real-World Case:

data_points

📚 Extend Your Knowledge

For a deeper dive into clustering algorithms, check out our Clustering Algorithms Guide.
🧠 Visual Insight:

cluster_centers