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  Influence of graph construction on graph-based clustering measures

Maier, M., von Luxburg, U., & Hein, M. (2009). Influence of graph construction on graph-based clustering measures. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1025-1032). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C483-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-DE62-6
Genre: Conference Paper

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 Creators:
Maier, M1, 2, Author              
von Luxburg, U1, 2, Author              
Hein, M, 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: Graph clustering methods such as spectral clustering are defined for general weighted graphs. In machine learning, however, data often is not given in form of a graph, but in terms of similarity (or distance) values between points. In this case, first a neighborhood graph is constructed using the similarities between the points and then a graph clustering algorithm is applied to this graph. In this paper we investigate the influence of the construction of the similarity graph on the clustering results. We first study the convergence of graph clustering criteria such as the normalized cut (Ncut) as the sample size tends to infinity. We find that the limit expressions are different for different types of graph, for example the r-neighborhood graph or the k-nearest neighbor graph. In plain words: Ncut on a kNN graph does something systematically different than Ncut on an r-neighborhood graph! This finding shows that graph clustering criteria cannot be studied independently of the kind of graph they are applied to. We also provide examples which show that these differences can be observed for toy and real data already for rather small sample sizes.

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 Dates: 2009-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5393
 Degree: -

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Title: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2008-12-08 - 2008-12-10

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Title: Advances in neural information processing systems 21
Source Genre: Proceedings
 Creator(s):
Koller, D, Editor
Schuurmans, D, Editor
Bengio, Y, Editor
Bottou, L, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1025 - 1032 Identifier: ISBN: 978-1-60560-949-2