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**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).

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