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.