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  Clustering stability: an overview

von Luxburg, U. (2010). Clustering stability: an overview. Foundations and Trends in Machine Learning, 2(3), 235-274. doi:10.1561/2200000008.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF1C-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6AE8-2
Genre: Journal Article

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von Luxburg, U1, 2, Author              
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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: A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most stable". In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications.

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 Dates: 2010-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.1561/2200000008
BibTex Citekey: 6333
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Title: Foundations and Trends in Machine Learning
Source Genre: Journal
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Publ. Info: Hanover, MA : Now Publishers
Pages: - Volume / Issue: 2 (3) Sequence Number: - Start / End Page: 235 - 274 Identifier: ISSN: 1935-8245
CoNE: https://pure.mpg.de/cone/journals/resource/19358245