Spectral clustering

Spectral clustering is a technique used in machine learning and data analysis for grouping data points into clusters based on the properties of the dataset. It leverages the eigenvalues and eigenvectors of matrices derived from the data, particularly the similarity matrix, to identify clusters. Here’s an overview of the key steps and concepts involved in spectral clustering: 1. **Similarity Graph**: First, a similarity graph is constructed from the data points.