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Spectral Clustering

Graph Laplacian eigenvectors reveal non-convex cluster structure

Original Points

k-means (direct)

Eigenvector Space (v₂ vs v₃)

Spectral Clustering

How it works: Build a k-NN graph on the points. Compute the normalized graph Laplacian L = I − D⁻¹/²AD⁻¹/². Its eigenvectors corresponding to small eigenvalues encode cluster membership. Running k-means in this eigenspace finds clusters that are connected but not necessarily convex.