Unsupervised classification (clustering) — how it differs from supervised.

• User supplies only a few parameters; no training sites.
• Computer finds statistical patterns (spectral clusters) in the data automatically.
• Spectral classes ≠ meaningful categories. The output is “Cluster 7”, not “Corn.”
• Post-labeling is required — the analyst assigns real-world labels to each cluster after the algorithm runs (often via the Recode tool).