Clustering examples into groups (example of Unsupervised learning):
- Pick examples (at random?) as exemplars
- Cluster remaining samples by minimizing distance between samples in same cluster (objective function) — put sample in group with closest exemplar
- Find median example in each cluster as new exemplar
- Repeat until there is no change
This works with unlabeled data, but if we had it labeled, we’d want to find a subsurface (e.g. for 2D data ⇒ line) of the data that naturally divides them.