K-Means Clustering

K-Means has the advantage that it’s pretty fast, as all we’re really doing is computing the distances between points and group centers; very few computations! It thus has a linear complexity O(n).

1. select a number of classes/groups to use and randomly initialize their respective center points.
2. classify each point to be in the group whose center is closest to it.
3. recompute the group center by taking the mean of all the vectors in the group.

Two failure cases for K-Means

source: The 5 Clustering Algorithms Data Scientists Need to Know