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For First Responders · Clustering Workbench

Clustering Workbench for a distribution fit

Built for first responders planning or training for real incidents. Run fast what-if scenarios for response planning and training — no software to install in the field. Simulate a distribution fit live below — adjust the inputs and watch it respond, right in your browser.

k-Means ClusteringLive

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.

▶ Run in Python

Data Inspector

Iteration0
Inertia0.00e+0
k4

Governing equation

Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.

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

Principal Component AnalysisLive

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.

▶ Run in Python

Data Inspector

Variance PC10.00
Variance PC20.00
PC1 explains0%
PC1 angle

Governing equation

Reading this result: PC1 explains 0% of the variance here; the stronger the correlation between features, the more PCA concentrates information on that first component.

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

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More with Clustering Workbench

Frequently asked questions

Is this good for first responders?
Yes — this version of "Clustering Workbench for a distribution fit" is framed for first responders planning or training for real incidents. Run fast what-if scenarios for response planning and training — no software to install in the field.
Do I need to install anything?
No. It runs in any modern browser, free, with no account required.