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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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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C483-0 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-DE62-6
Genre: Konferenzbeitrag

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externe Referenz:
https://papers.nips.cc/paper/3496-influence-of-graph-construction-on-graph-based-clustering-measures.pdf (Verlagsversion)
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 Urheber:
Maier, M1, 2, Autor           
von Luxburg, U1, 2, Autor           
Hein, M, Autor           
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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Schlagwörter: -
 Zusammenfassung: 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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 Datum: 2009-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 5393
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2008-12-08 - 2008-12-10

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Titel: Advances in neural information processing systems 21
Genre der Quelle: Konferenzband
 Urheber:
Koller, D, Herausgeber
Schuurmans, D, Herausgeber
Bengio, Y, Herausgeber
Bottou, L, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1025 - 1032 Identifikator: ISBN: 978-1-60560-949-2