Add link to show k-means (#25732)
Link gives an interactive visualization of how the k-means algorithm works.
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@ -99,6 +99,8 @@ plt.show()
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Since the data points belong usually to a high-dimensional space, the similarity measure is often defined as a distance between two vectors (Euclidean, Manhathan, Cosine, Mahalanobis...)
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Here's a visualization of K-means that allows you to change the number of clusters and centroids to show how k data points converge into clusters around the closest centroid: [Visualizing K-Means](http://stanford.edu/class/ee103/visualizations/kmeans/kmeans.html)
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### Mixture Density
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We can write *mixture density* as:
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