Types of Unsupervised Learning: K-Means Clustering

The Clustering Algorithm that's Not as Unsupervised as its Name Suggests

K-Means Clustering is a type of unsupervised learning where we don't really know what's going on.

Imagine you're at a crowded music festival, and you want to figure out what genre of music each crowd is most likely to be enjoying.

Collect a dataset of festival-goers with their favorite bands
Use K-Means Clustering to group similar music tastes together
Voila! You have a map of the festival grounds with clusters of indie, rock, and electronic music fans

1. Initialize centroids (or cluster centers) randomly

2. Assign each data point to the cluster with the closest centroid

3. Recalculate the centroid as the average of all data points in each cluster

4. Repeat until convergence or until you get bored

And that's it! You've successfully used K-Means Clustering to group your music festival crowd by genre.