K means clustering

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K means clustering

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Last edited Sep 8, 2020

Created on Sep 8, 2020

This example shows the k-means clustering algorithm in action, animating the iterative process of grouping a small set of points from `data.csv` into three clusters. Initially, three randomly placed centroids are drawn, and each iteration assigns points to the nearest centroid using Euclidean distance, then moves the centroids to the cluster mean. The visualization uses D3 v6’s selection and `transition` methods to animate the points and centroids, with the centroid styled distinctly via a black stroke. The iteration count is displayed as text, and the animation stops after ten iterations.

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