Name
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 kfor 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-75225-7_18 | ALT |
Keywords | Field | DocType |
cluster identification,sample point,underlying distribution,different property,distinct cluster,cluster identification problem,symmetric k-nearest-neighbor graph,neighborhood graph,nearest-neighbor graphs,symmetric nearest-neighbor graph,different cluster,k nearest neighbor,nearest neighbor graph | k-nearest neighbors algorithm,Discrete mathematics,Cluster (physics),Graph,Combinatorics,Random graph,Computer science,Nearest-neighbor chain algorithm,Artificial intelligence,Machine learning,Parameter identification problem | Conference |
Volume | ISSN | Citations |
4754 | 0302-9743 | 13 |
PageRank | References | Authors |
1.13 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Maier | 1 | 91 | 7.26 |
| Matthias Hein | 2 | 663 | 62.80 |
| von luxburg | 3 | 3246 | 170.11 |