Statistics and Computing

, Volume 17, Issue 4, pp 395–416

A tutorial on spectral clustering


DOI: 10.1007/s11222-007-9033-z

Cite this article as:
von Luxburg, U. Stat Comput (2007) 17: 395. doi:10.1007/s11222-007-9033-z


In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.


Spectral clustering Graph Laplacian 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Max Planck Institute for Biological CyberneticsTübingenGermany

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