For K-12 Students · k-Means Clustering
k-Means Clustering for a clustering model
Built for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework. Simulate a clustering model live below — adjust the inputs and watch it respond, right in your browser.
k-Means ClusteringLive
Lloyd's algorithm · live
Controls
Presets
Lloyd's algorithm: assign each point to its nearest centroid, then move each centroid to the mean of its members. Repeat until stable. Try setting k different from the true cluster count.
Data Inspector
Iteration0
Inertia0.00e+0
k4
Governing equation
Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
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Frequently asked questions
- Is this good for k-12 students?
- Yes — this version of "k-Means Clustering for a clustering model" is framed for k-12 students learning it in middle or high school. Watch the idea come alive with plain-language steps and everyday examples — perfect for projects and homework.
- Do I need to install anything?
- No. It runs in any modern browser, free, with no account required.