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.
| Author(s): | Maier, M. and Hein, M. and von Luxburg, U. |
| Links: | |
| Book Title: | ALT 2007 |
| Journal: | Algorithmic Learning Theory: Proceedings of the 18th International Confererence (ALT 2007) |
| Pages: | 196-210 |
| Year: | 2007 |
| Month: | October |
| Day: | 0 |
| Editors: | Hutter, M. , R. A. Servedio, E. Takimoto |
| Publisher: | Springer |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/978-3-540-75225-7_18 |
| Event Name: | 18th International Conference on Algorithmic Learning Theory |
| Event Place: | Sendai, Japan |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@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}
}