Cluster identification in nearest neighbor graphs.
Assume we are given a sample of points from some under- lying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clus- ters 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 prob- ability 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.