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A related algorithm is the '''[[Meila–Shi algorithm]]'''<ref>Marina Meilă & Jianbo Shi, "[http://www.citeulike.org/user/mpotamias/article/498897 Learning Segmentation by Random Walks]", Neural Information Processing Systems 13 (NIPS 2000), 2001, pp. 873–879.</ref>, which takes the [[eigenvector]]s corresponding to the ''k'' largest [[eigenvalue]]s of the matrix <math>P = SD^{-1}</math> for some ''k'', and then invokes another algorithm (e.g. [[k-means clustering]]) to cluster points by their respective ''k'' components in these eigenvectors.
An efficiency improvement of spectral clustering is the '''[[spectral neighborhood (SPAN) algorithm]]'''<ref>Liangcai Shu, Aiyou Chen, Ming Xiong, Weiyi Meng, "[http://www.cs.binghamton.edu/~meng/pub.d/ICDE11_conf_full_065_update.pdf Efficient Spectral Neighborhood Blocking for Entity Resolution]", IEEE International Conference on Data Engineering (ICDE), pp.1067-1078, Hannover, Germany, April 2011.</ref>, which performs spectral clustering without explicitly computing the similarity matrix, and therefore dramatically improves the scalability of the standard spectral clustering algorithm.
== Relationship with ''k''-means ==
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