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Simulators

K-means clustering simulator

k-means alternates two half-steps: assign each point to its nearest centroid, then move each centroid to the middle of its points. Step through them until nothing changes.

The simulator

K-means clustering simulator: how to use it

Choose k and try other starting centroids to see that the final clusters can depend on where you start.

Lessons that use it

  • Clustering with k-means (Machine Learning)Unsupervised learning: group unlabelled points by alternating two steps, assign and update; why the start matters; and choosing k with the elbow method.

See also