Abstract Linear Algebra II
Spectral clustering is a technique that uses the eigenvalues and eigenvectors of a similarity matrix to reduce dimensionality before performing clustering. By transforming the data into a lower-dimensional space, it enables more effective grouping based on the underlying structure of the data, making it particularly useful in identifying clusters that are not necessarily spherical or evenly sized. This approach bridges concepts from spectral theory and linear algebra, especially in contexts like computer science and data analysis.
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