Spectral clustering is an alternative clustering technique that is rooted in graph theory. It works by constructing an adjacency matrix that connects similar points within a neighborhood defined by something like an epsilon radius or k-neighbors. It then projects the data into a lower dimensional space and applies a traditional clustering technique like K-Means on the projected data. Spectral clustering is particularly well-suited for finding non-convex clusters, such as the case where one cluster is embedded around a ring of data points that form a separate cluster.