K-Means Clustering Visualizer
An unsupervised learning algorithm that groups similar data points into K clusters based on their distance to centroids.
Click on the canvas to add custom data points.
📜 Algorithm State
Ready. Add points or generate random ones, then click Next Step.
📍 Centroids (Mean Positions)
💡 How it works
- Initialize K centroids randomly.
- Assignment: Each point joins the nearest centroid's cluster.
- Update: Centroids move to the center (mean) of their assigned points.
- Repeat until centroids stop moving.