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Add link to show k-means (#25732)

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Link gives an interactive visualization of how the k-means algorithm works.
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spinder96
2018-12-21 21:20:49 -05:00
committed by Manish Giri
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commit 56a42de7b1
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guide/english/machine-learning/clustering-algorithms/index.md
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@ -99,6 +99,8 @@ plt.show()
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...) 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...)
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)
### Mixture Density ### Mixture Density
We can write *mixture density* as: We can write *mixture density* as:
![mixture density](https://latex.codecogs.com/gif.latex?p%28x%29%20%3D%20%5Csum_%7Bi%3D1%7D%5E%7Bk%7Dp%28x%7CG_%7Bi%7D%29p%28G_%7Bi%7D%29) ![mixture density](https://latex.codecogs.com/gif.latex?p%28x%29%20%3D%20%5Csum_%7Bi%3D1%7D%5E%7Bk%7Dp%28x%7CG_%7Bi%7D%29p%28G_%7Bi%7D%29)