Analyzing High-Dimensional Data by Subspace Validity

Kurzanzeige
dc.contributor.authorAmir, Amihooddeu
dc.contributor.authorKashi, Reuvendeu
dc.contributor.authorNetanyahu, Nathan S.deu
dc.contributor.authorKeim, Daniel A.
dc.contributor.authorWawryniuk, Markusdeu
dc.date.accessioned2011-03-24T15:55:49Zdeu
dc.date.available2011-03-24T15:55:49Zdeu
dc.date.issued2003
dc.description.abstractWe are proposing a novel method that makes it possible to analyze high dimensional data with arbitrary shaped projected clusters and high noise levels. At the core of our method lies the idea of subspace validity. We map the data in a way that allows us to test the quality of subspaces using statistical tests. Experimental results, both on synthetic and real data sets, demonstrate the potential of our method.eng
dc.description.versionpublished
dc.format.mimetypeapplication/pdfdeu
dc.identifier.citationFirst publ. in: Proceedings / Third IEEE International Conference on Data Mining, ICDM 2003 : 19 - 22 November 2003, Melbourne, Florida, pp. 473-476deu
dc.identifier.doi10.1109/ICDM.2003.1250955
dc.identifier.ppn302286144deu
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/5491
dc.language.isoengdeu
dc.legacy.dateIssued2009deu
dc.rightsAttribution-NonCommercial-NoDerivs 2.0 Generic
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/
dc.subject.ddc004deu
dc.titleAnalyzing High-Dimensional Data by Subspace Validityeng
dc.typeINPROCEEDINGSdeu
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@inproceedings{Amir2003Analy-5491,
  year={2003},
  doi={10.1109/ICDM.2003.1250955},
  title={Analyzing High-Dimensional Data by Subspace Validity},
  isbn={0-7695-1978-4},
  publisher={IEEE Comput. Soc},
  booktitle={Third IEEE International Conference on Data Mining},
  pages={473--476},
  author={Amir, Amihood and Kashi, Reuven and Netanyahu, Nathan S. and Keim, Daniel A. and Wawryniuk, Markus}
}
kops.citation.iso690AMIR, Amihood, Reuven KASHI, Nathan S. NETANYAHU, Daniel A. KEIM, Markus WAWRYNIUK, 2003. Analyzing High-Dimensional Data by Subspace Validity. Third IEEE International Conference on Data Mining. Melbourne, FL, USA. In: Third IEEE International Conference on Data Mining. IEEE Comput. Soc, 2003, pp. 473-476. ISBN 0-7695-1978-4. Available under: doi: 10.1109/ICDM.2003.1250955deu
kops.citation.iso690AMIR, Amihood, Reuven KASHI, Nathan S. NETANYAHU, Daniel A. KEIM, Markus WAWRYNIUK, 2003. Analyzing High-Dimensional Data by Subspace Validity. Third IEEE International Conference on Data Mining. Melbourne, FL, USA. In: Third IEEE International Conference on Data Mining. IEEE Comput. Soc, 2003, pp. 473-476. ISBN 0-7695-1978-4. Available under: doi: 10.1109/ICDM.2003.1250955eng
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