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  Cluster Identification in Nearest-Neighbor Graphs

Maier, M., Hein, M., & von Luxburg, U.(2007). Cluster Identification in Nearest-Neighbor Graphs (163). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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MPIK-TR-163.pdf (Publisher version), 307KB
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 Creators:
Maier, M1, 2, Author           
Hein, M1, 2, Author           
von Luxburg, U1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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 ``identifiedlsquo;lsquo;: 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 ``optimallsquo;lsquo; 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.

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 Dates: 2007-05
 Publication Status: Published in print
 Pages: 19
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 163
BibTex Citekey: 4587
 Degree: -

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 163 Sequence Number: - Start / End Page: - Identifier: -