Spectral clustering is a machine learning technique used to partition data points into clusters based on the eigenvalues of a similarity matrix. It works by representing the data points as nodes in a graph, where the edges between nodes represent the similarity between them. By analyzing the eigenvalues of the Laplacian matrix of the graph, spectral clustering can identify natural clusters within the data. Unlike traditional clustering algorithms that rely on distance metrics, spectral clustering is able to capture complex patterns and non-linear relationships in the data. It is commonly used in applications such as image segmentation, community detection in social networks, and document clustering.