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==Software==
Free software implementing spectral clustering is available in large open source projects like [[scikit-learn]]<ref>{{Cite web|url=http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering|title = 2.3. Clustering}}</ref> using [[LOBPCG]]<ref>{{Cite conference | url = https://www.researchgate.net/publication/343531874 | title = Modern preconditioned eigensolvers for spectral image segmentation and graph bisection | conference = Clustering Large Data Sets; Third IEEE International Conference on Data Mining (ICDM 2003) Melbourne, Florida: IEEE Computer Society| editor = Boley| editor2 = Dhillon| editor3 = Ghosh| editor4 = Kogan | pages = 59–62| year = 2003| last1 = Knyazev| first1 = Andrew V.}}</ref> with [[multigrid]] [[preconditioning]]<ref name="spectralmultigrid2006">{{Cite conference | url = https://www.researchgate.net/publication/354448354 | title = Multiscale Spectral Image Segmentation Multiscale preconditioning for computing eigenvalues of graph Laplacians in image segmentation | conference = Fast Manifold Learning Workshop, WM Williamburg, VA| year = 2006| last1 = Knyazev| first1 = Andrew V. | doi=10.13140/RG.2.2.35280.02565}}</ref> <ref>{{Cite conference | url = https://www.researchgate.net/publication/343531874 | title = Multiscale Spectral Graph Partitioning and Image Segmentation | conference = Workshop on Algorithms for Modern Massive Datasets Stanford University and Yahoo! Research| year = 2006| last1 = Knyazev| first1 = Andrew V.}}</ref> or [[ARPACK]], [[Apache Spark#MLlib Machine Learning Library
== Relationship with other clustering methods ==
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