@inproceedings{4590,
  title = {Cluster Identification in Nearest-Neighbor Graphs},
  journal = {Algorithmic Learning Theory: Proceedings of the 18th International Confererence (ALT 2007)},
  booktitle = {ALT 2007},
  abstract = {Assume we are given a sample of points from some underlying
  distribution which contains several distinct clusters. Our goal is
  to construct a neighborhood graph on the sample points such that
  clusters are ``identified‘‘: that is, the subgraph induced by points
  from the same cluster is connected, while subgraphs corresponding to
  different clusters are not connected to each other. We derive bounds
  on the probability that cluster identification is successful, and
  use them to predict ``optimal‘‘ values of k for the mutual and
  symmetric k-nearest-neighbor graphs. We point out different
  properties of the mutual and symmetric nearest-neighbor graphs
  related to the cluster identification problem.},
  pages = {196-210},
  editors = {Hutter, M. , R. A. Servedio, E. Takimoto},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {2007},
  author = {Maier, M. and Hein, M. and von Luxburg, U.},
  doi = {10.1007/978-3-540-75225-7_18},
  month_numeric = {10}
}