Spectral Clustering

Spectral clustering computes the second largest eigenvalue of the Laplacian of the similarity matrix to decide where to partition the similarity matrix. The resulting clusters may then be re-partitioned in order to generate a hierarchy. Efficient numeric algorithms solve the underlying linear algebra problems to facilitate spectral clustering on large datasets.