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  From outliers to prototypes: Ordering data

Harmeling, S., Dornhege, G., Tax, D., Meinecke, F., & Müller, K.-R. (2006). From outliers to prototypes: Ordering data. Neurocomputing, 69(13-15), 1608-1618. doi:10.1016/j.neucom.2005.05.015.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D089-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-8368-3
Genre: Journal Article

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Harmeling, S1, Author              
Dornhege, G, Author
Tax, D, Author
Meinecke, F, Author
Müller, K-R1, Author              
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1External Organizations, ou_persistent22              

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 Abstract: We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach.

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 Dates: 2006-08
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neucom.2005.05.015
BibTex Citekey: 6348
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Title: Neurocomputing
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 69 (13-15) Sequence Number: - Start / End Page: 1608 - 1618 Identifier: ISSN: 0925-2312
CoNE: https://pure.mpg.de/cone/journals/resource/954925566733