Set of techniques to find components that belong together.
Note: Grouping is how the human visual system perceives things and clustering is the actual algorithm itself.
We want to assign examples to “groups”.
- K-means (most popular)
- density-based clustering
- Ensemble Clustering
- Like random forest but for voting for clustering
- This is problematic because of the label switching problem – we can get clustering with permuted labels on each initialisation
- Don’t vote on what specific class each cluster is
- Instead, vote on whether points are in the same cluster (label independent)
- Then, come up with labels after voting
- hierarchical clustering