From 56a42de7b109a001991d1abe55b1bb2c2d104129 Mon Sep 17 00:00:00 2001 From: spinder96 Date: Fri, 21 Dec 2018 21:20:49 -0500 Subject: [PATCH] Add link to show k-means (#25732) Link gives an interactive visualization of how the k-means algorithm works. --- guide/english/machine-learning/clustering-algorithms/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/guide/english/machine-learning/clustering-algorithms/index.md b/guide/english/machine-learning/clustering-algorithms/index.md index 60ea6d9400..67395c482f 100644 --- a/guide/english/machine-learning/clustering-algorithms/index.md +++ b/guide/english/machine-learning/clustering-algorithms/index.md @@ -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...) +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 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)