Spectral clustering is a technique used in data analysis to group similar data points into clusters. It works by transforming the data into a lower-dimensional space using the eigenvalues and eigenvectors of a similarity matrix. This transformation helps to reveal the underlying structure of the data, making it easier to identify distinct clusters. After the transformation, traditional clustering methods, like k-means, are applied to the new representation of the data. This approach is particularly effective for complex datasets where clusters may not be spherical or evenly sized, allowing for more accurate and meaningful groupings.