SpectralIn clusteringthe istrivial alsocase relatedof to [[DBSCAN]] clustering, that finds density-connected components.determining [[Connected component (graph theory)|Connectedconnected graph components]] correspond— tothe optimal spectral clusters (with no edges cut);and— DBSCANspectral usesclustering anis asymmetricalso neighborrelated graphto with[[DBSCAN]] edgesclustering, removedthat whenfinds sourcedensity-connected points are not densecomponents.<ref>{{Cite conference|last1=Schubert|first1=Erich|last2=Hess|first2=Sibylle|last3=Morik|first3=Katharina|date=2018|title=The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering|url=http://ceur-ws.org/Vol-2191/paper38.pdf|conference=LWDA|pages=330–334}}</ref> Thus, DBSCAN is a special case of spectral clustering, but one which allows more efficient algorithms (worst case <math>O(n^2)</math>, in many practical cases much faster with indices).
