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  A Tutorial on Spectral Clustering

von Luxburg, U. (2007). A Tutorial on Spectral Clustering. Statistics and Computing, 17(4), 395-416. doi:10.1007/s11222-007-9033-z.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CADF-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7A47-5
Genre: Journal Article

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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: In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

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 Dates: 2007-12
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1007/s11222-007-9033-z
BibTex Citekey: 4488
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Title: Statistics and Computing
Source Genre: Journal
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Publ. Info: Norwell, MA : Kluwer Academic Publishers
Pages: - Volume / Issue: 17 (4) Sequence Number: - Start / End Page: 395 - 416 Identifier: ISSN: 0960-3174
CoNE: https://pure.mpg.de/cone/journals/resource/954926992159