Week 5: Clustering & Dimensionality Reduction

Learning Objectives

• 05A I can apply k-means clustering to group data and interpret the resulting cluster assignments.
• 05B I can evaluate clustering quality using the elbow method and silhouette score to choose an appropriate k.
• 05C I can apply PCA to reduce dimensionality and interpret how much variance each component explains.

Perspectival Reading

Reading: TBD

Reflection Questions

1. Clustering imposes structure on data — what happens when the groups we find reflect historical inequities?
2. PCA finds directions of maximum variance. Whose variation is centered, and whose is treated as noise?
3. Unsupervised methods have no ground truth. How should that affect our confidence in their outputs?

Materials

• Monday class
• Wednesday class