Spectral clustering: Difference between revisions

Revision as of 08:27, 6 August 2021

Revision as of 19:09, 8 September 2021

Line 44:

}}</ref>

==Software==

Free software to implement spectral clustering is available in large open source projects like [[Scikit-learn]]<ref>http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering</ref> using [[LOBPCG]]<ref>{{cite conference

Free software implementing spectral clustering is available in large open source projects like [[Scikit-learn]]<ref>http://scikit-learn.org/stable/modules/clustering.html#spectral-clustering</ref> using [[LOBPCG]]<ref>{{Cite conference | url = https://www.researchgate.net/publication/343531874_Modern_Preconditioned_Eigensolvers_for_Spectral_Image_Segmentation_and_Graph_Bisection | 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_Multiscale_Spectral_Image_Segmentation_Multiscale_preconditioning_for_computing_eigenvalues_of_graph_Laplacians_in_image_segmentation | 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_Modern_Preconditioned_Eigensolvers_for_Spectral_Image_Segmentation_and_Graph_Bisection | 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\MLlib|MLlib]] for pseudo-eigenvector clustering using the [[power iteration]] method,<ref>http://spark.apache.org/docs/latest/mllib-clustering.html#power-iteration-clustering-pic</ref> and [[R (programming language)|R]].<ref>https://cran.r-project.org/web/packages/kernlab</ref>

|title=Modern Preconditioned Eigensolvers for Spectral Image Segmentation and Graph Bisection

|last1=Knyazev |first1=Andrew V.

|conference=Workshop on Clustering Large Data Sets Third IEEE International Conference on Data Mining (ICDM 2003)

|year=2003

|url=https://www.researchgate.net/publication/240624263

}}</ref> with [[multigrid]] [[preconditioning]],<ref name="spectralmultigrid2006">

{{cite conference

|title=Multiscale Spectral Graph Partitioning and Image Segmentation

|last1=Knyazev |first1=Andrew V.

|conference=[http://www.stat.uchicago.edu/~lekheng/meetings/mmds/posters2006.html Workshop on Algorithms for Modern Massive Data Sets Stanford University and Yahoo! Research]

|year=2006

}}</ref> or [[ARPACK]], [[Apache Spark#MLlib Machine Learning Library\MLlib|MLlib]] for pseudo-eigenvector clustering using the [[power iteration]] method,<ref>http://spark.apache.org/docs/latest/mllib-clustering.html#power-iteration-clustering-pic</ref> and [[R (programming language)|R]].<ref>https://cran.r-project.org/web/packages/kernlab</ref>

== Relationship with other clustering methods ==