@techreport{4139,
  title = {A tutorial on spectral clustering},
  abstract = {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. Nevertheless, on the first glance spectral
  clustering looks a bit mysterious, and it is not obvious to see why it
  works at all and what it really does. This article is a tutorial
  introduction to spectral clustering. 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.},
  number = {149},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tübingen},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2006},
  slug = {4139},
  author = {von Luxburg, U.},
  month_numeric = {8}
}