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Use case · powered by k-Means Clustering

k-Means Clustering for a pathfinding AI

Simulate a pathfinding AI live in your browser. This runs the real k-Means Clustering solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

k-Means ClusteringLive

Controls

Presets

k-means alternates two steps: assign each point to its nearest centroid, then move each centroid to the mean of its points. Repeat and the clusters snap into place. Choosing the right k, and where to start, both matter. Educational tool.

▶ Run in Python

Data Inspector

Clusters3
Points80
Statusconverged

Governing equation

Reading this result: k = 3 is fewer than the 4 true blobs, so Lloyd’s algorithm is forced to merge neighbouring groups into one cluster and real structure is lost. Try k = 4.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full k-Means Clustering tool models a pathfinding AI with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with k-Means Clustering

Other ways to simulate a pathfinding AI

Frequently asked questions

How do I simulate a pathfinding AI?
Open this page and use the live k-Means Clustering tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive a pathfinding AI from your own measurements.