MAS-II: Clustering and PCA depend on the geometry you create
Unsupervised methods work with relationships among predictors, not a labelled outcome. Scaling and distance choices can dominate the result. Interpret a cluster as a pattern in the selected representation, not as a proven biological or business category.
Worked example or practice scenario
If one feature spans 0–100,000 and another spans 0–1, Euclidean distance on raw units can mostly reflect the first feature. Standardisation changes the geometry. Likewise, PCA on covariance and correlation matrices can produce different directions.
Try this next
Explain the preprocessing choice and compare stability under a sensible alternative. For clustering, examine whether the pattern persists rather than assigning causal meaning to a cluster label. The current MAS-II outline explicitly includes statistical learning; check the sitting’s full tasks.
Reading sources
ActNet editorial guide · October 1, 2026 · Original illustrative examples.