Spectral Clustering is Just K-Means on Eigenvectors

Spectral clustering isn't a new algorithm, it's plain k-means in disguise. K-means slices straight through two concentric rings and fails, because it only sees distance to a center. So spectral clustering changes where the points live first: connect each point to its neighbors, build the graph Laplacian (degree minus weights), and read off its smallest eigenvectors. Those eigenvectors hand every point new coordinates where the inner ring collapses to a blob and the outer ring drifts away, so the very same straight-line k-means that failed now splits them perfectly. Want more clusters? Keep more eigenvectors. #shorts

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Видео Spectral Clustering is Just K-Means on Eigenvectors канала DataMListic