Do I need to install anything?

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

For Students · k-Means Clustering

k-Means Clustering for a hash table

Built for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition. Simulate a hash table 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.

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Frequently asked questions

Is this good for students?: Yes — this version of "k-Means Clustering for a hash table" is framed for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.