K-Means

Watch K-Means clustering in action: adjust parameters, generate data, and see the algorithm find natural groupings in real-time

Description:

This interactive visualization demonstrates the K-Means clustering algorithm from the ground up. Adjust the number of clusters (K) and data points, then watch the algorithm iterate through:

• Random initialization of cluster centroids.

• Assignment step where each point is assigned to its nearest centroid.

• Update step where centroids move to the mean of assigned points.

• Convergence as the process stabilizes.

Built with vanilla JavaScript implementing the full K-Means algorithm (no ML libraries), this visualization shows:

• Real-time centroid movement across iterations.

• Color-coded cluster assignments updating dynamically.

• Iteration count and convergence metrics.

• The effect of different K values on cluster quality.

Technical highlights:

• Euclidean distance calculations for nearest-neighbor assignment.

• Lloyd's algorithm implementation with configurable max iterations.

• Animated visualization of the iterative optimization process.

• Demonstrates understanding of unsupervised learning fundamentals.

Key insight: Watch how the algorithm can get stuck in local minima with poor initialization, and how different K values affect the clustering quality, critical considerations when applying K-Means to real-world datasets.