Visualizing K-Means Clustering

Mean square point-centroid distance: not yet calculated
The $k$-means algorithm is an iterative method for clustering a set of $N$ points (vectors) into $k$ groups or clusters of points.


Repeat until convergence:
Find closest centroid
Find the closest centroid to each point, and group points that share the same closest centroid.
Update centroid
Update each centroid to be the mean of the points in its group.
Find closest centroid


Clustered points
Number of clusters
Number of centroids

New points New centroids