Spectral Clustering is a technique used in machine learning to group data points into clusters based on their similarities. It works by first constructing a similarity graph from the data, where each point is a node and edges represent the relationships between them. The algorithm then computes the eigenvalues and eigenvectors of the graph's Laplacian matrix, which helps identify the underlying structure of the data. After obtaining the eigenvectors, Spectral Clustering reduces the dimensionality of the data and applies a standard clustering algorithm, like K-means, to the transformed data. This approach is particularly effective for identifying clusters that are not necessarily spherical in shape, making it versatile for various applications.