Spectral Clustering: Algorithm Implementation and Comparative Analysis
Spectral clustering identifies arbitrarily shaped sample spaces and converges to global optimal solutions by performing eigen decomposition on similarity matrices to obtain eigenvectors for clustering. This program implements multiple clustering algorithms: Q-matrix clustering, k-means clustering, first eigencomponent clustering, second generalized eigencomponent clustering, shared data generation, and neighborhood matrix generation. Code implementation includes similarity matrix construction using Gaussian kernel functions, eigenvalue decomposition via scipy.linalg.eig, and comparative evaluation metrics.