*2.3. Spectral Clustering*

Spectral clustering is a technique whose goal is to cluster data that is connected, but not necessarily clustered within convex boundaries, so it has no limitations on the shape of data and can detect linearly non-separable patterns. The basic idea is to construct a weighted graph from the initial dataset where each node represents a pattern, and each weighted edge simply considers the similarity between two patterns [13]. In this context, this clustering problem can be seen as a graph cut problem, which can be tackled by means of the spectral graph theory. The core of this theory is the eigenvalue decomposition of the Laplacian matrix of the weighted graph obtained from data. The pseudocode is:
