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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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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CDCB-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8772-5
Genre: Report

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