Hands-On Machine Learning with C++

Spectral clustering algorithms

Spectral clustering refers to all methods that divide a set of data into clusters using the eigenvectors of the adjacency matrix of a graph or other matrices derived from it. An adjacency matrix describes a complete graph with vertices in objects and edges between each pair of objects with a weight corresponding to the degree of similarity between these vertices. Spectral clustering is a transformation of the initial set of objects into a set of points in space whose coordinates are elements of eigenvectors. The formal name of such a task is the normalized cuts problem. The resulting set of points is then clustered using standard methods—for example, with the k-means algorithm. Changing the representation created ...