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2007
Springer

Cluster Identification in Nearest-Neighbor Graphs

8 years 11 months ago
Cluster Identification in Nearest-Neighbor Graphs
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.
Markus Maier, Matthias Hein, Ulrike von Luxburg
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2007
Where ALT
Authors Markus Maier, Matthias Hein, Ulrike von Luxburg
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