Clustering Space
Unassigned
Centroid
Centroid Trail
Voronoi Regions
Assign xᵢ to argmin_k ||xᵢ - μₖ||²
Each point joins nearest centroid
Inertia (Within-Cluster SS)
Elbow Method
J = Σₖ Σᵢ∈Cₖ ||xᵢ - μₖ||²
Total within-cluster variance
Data Points
Click on the clustering canvas to add points
Number of Clusters (K)
3
Clusters
Algorithm Control
0
Iteration
Statistics
Total Points
0
Inertia
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Quick Examples
How it works: K-Means alternates between (1) assigning each point to its nearest centroid, and (2) updating centroids to the mean of assigned points. It converges when assignments no longer change.