K-Means Clustering Visualizer

An interactive, step-by-step playground to demystify unsupervised vector quantization.

💡 Quick Tip: If the algorithm isn't running, you can click directly on the canvas to manually place data points before hitting "Initialize"!
Current Phase: Setup

Generate custom data distributions, choose your $K$ cluster value, and click "Initialize" to launch the model execution tracker.

  • 1. Place $K$ target centroids randomly
  • 2. Assign every node to closest centroid
  • 3. Recalculate and shift center vectors
  • 4. Converged! (Centroids stop moving)