K-Means Clustering Playground
K-means groups unlabeled points into k clusters. Click on the canvas to add points, pick k, then watch the algorithm step by step: assign each point to the nearest centroid, move the centroids to the mean, repeat until nothing changes.
Clustering Visualization
not started
Controls
Number of clusters k
Points
0
SSE (inertia)
—
Iterations
0
Converged
—
How it works: K-means minimizes the sum
of squared distances (SSE) between each point and its
cluster's centroid. Each Step does one
full pass: assign every point to the nearest centroid,
then move each centroid to the average of its points.
When no point changes cluster, the algorithm has
converged. K-means is fast but sensitive to the starting
centroids — try Generate data, change
k, and compare the SSE you end up with.
What's happening here? Imagine sorting
a pile of mixed coins by eye: you pick a few "reference"
coins, put every coin next to the closest reference,
then move each reference to the center of its pile, and
repeat. That's k-means. The centroids (white cores with
colored rings) are the references. Notice how clusters
with different k produce very different groupings — the
algorithm finds a grouping, not necessarily
the grouping, and k is something you choose
from domain knowledge.
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