K-Means Clustering - Interactive Visualization

K-Means is the most widely used clustering algorithm in machine learning. It assigns each data point to the nearest centroid, then moves centroids to the center of their cluster - repeating until nothing changes. This interactive demo lets you watch each step, click to add points, and see how different initializations lead to different results.

• Step through each iteration: assignment → centroid update → check convergence
• Click the canvas to add custom data points and watch the clusters adapt
• See Voronoi region boundaries - the decision boundaries K-Means creates
• Try running multiple times with "Initialize" to see sensitivity to starting position
• Inertia (within-cluster sum of squared distances) measures cluster quality
• Used in: customer segmentation, image compression, feature quantization, recommendation systems

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.