Spectral Clustering uses the eigenvalues of the similarity matrix of data to reduce the data's dimensionality and then clusters it in fewer dimensions

It draws inspiration from graph theory and the concept of identifying groups of nodes based on the edges connecting them

Spectral Clustering is useful when the structure of a cluster is highly non-convex or when a measure of the center and spread of a cluster does not describe a complete cluster

It is possible to use a similar process without implementing spectral clustering by transforming data to a lower dimension and then applying K-Means or Gaussian Mixtures on the linearly separable data