Spectral clustering is a family of techniques in multivariate statistics that use the spectrum, or eigenvalues, of a data set's similarity matrix to perform dimensionality reduction before clustering in fewer dimensions. The similarity matrix, provided as an input, gives a quantitative assessment of the relative similarity of every pair of points in the data set, and when applied to image segmentation the technique is known as segmentation-based object categorization.
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1. Spectral clustering (Wikipedia)
Wikipedia article body, read for Browse By backfill (w-bbfill-computing6-0927)
scikit-learn: scikit-learn: Clustering
scikit-learnIn Group: Clustering Algorithms, Section 2.3.6 Spectral clusteringQuote, In Group: Clustering Algorithms, Section 2.3.6 Spectral clustering
SpectralClustering performs a low-dimension embedding of the affinity matrix between samples, followed by clustering, e.g., by KMeans, of the components of the eigenvectors in the low dimensional space.
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