Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Researchers · k-Means Clustering

k-Means Clustering for a neural network

Built for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model. Simulate a neural network live below — adjust the inputs and watch it respond, right in your browser.

k-Means ClusteringLive

finding groups in data

Controls

Presets

Number of clusters k

Iterations

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

J = k = 1 \sum 3 x \in C_{k} \sum ∥ x - μ_{k} ∥^{2}, μ_{k} = \frac{1}{∣ C _{k} ∣} x \in C_{k} \sum x

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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Frequently asked questions

Is this good for researchers?: Yes — this version of "k-Means Clustering for a neural network" is framed for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.