Clustering Workbench for a dice-fairness check
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 dice-fairness check live below — adjust the inputs and watch it respond, right in your browser.
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
Governing equation
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
Controls
Presets
PCA finds the directions along which data varies most. The eigenvectors of the covariance matrix are the principal components (cyan = most variance, green = least), and their eigenvalues are the variances along each. Projecting onto the top components is the basis of dimensionality reduction.
Data Inspector
Governing equation
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
More with Clustering Workbench
Frequently asked questions
- Is this good for students?
- Yes — this version of "Clustering Workbench for a dice-fairness check" 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.