Analyzing High-Dimensional Data by Subspace Validity

Zitieren

Dateien zu dieser Ressource

Prüfsumme: MD5:94cad39177d982c23eaad159bc4670c3

AMIR, 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. Third IEEE International Conference on Data Mining. Melbourne, FL, USA. IEEE Comput. Soc, pp. 473-476. ISBN 0-7695-1978-4

@inproceedings{Amir2003Analy-5491, title={Analyzing High-Dimensional Data by Subspace Validity}, year={2003}, doi={10.1109/ICDM.2003.1250955}, 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} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/5491"> <dc:creator>Kashi, Reuven</dc:creator> <dc:creator>Netanyahu, Nathan S.</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:49Z</dcterms:available> <dcterms:rights rdf:resource="https://creativecommons.org/licenses/by-nc-nd/2.0/legalcode"/> <dc:contributor>Kashi, Reuven</dc:contributor> <dc:contributor>Netanyahu, Nathan S.</dc:contributor> <dc:language>eng</dc:language> <dc:creator>Wawryniuk, Markus</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5491"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:49Z</dc:date> <dcterms:abstract xml:lang="eng">We 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.</dcterms:abstract> <dc:format>application/pdf</dc:format> <dcterms:issued>2003</dcterms:issued> <dc:contributor>Amir, Amihood</dc:contributor> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:title>Analyzing High-Dimensional Data by Subspace Validity</dcterms:title> <dc:rights>deposit-license</dc:rights> <dcterms:bibliographicCitation>First publ. in: Proceedings / Third IEEE International Conference on Data Mining, ICDM 2003 : 19 - 22 November 2003, Melbourne, Florida, pp. 473-476</dcterms:bibliographicCitation> <dc:creator>Amir, Amihood</dc:creator> <dc:contributor>Wawryniuk, Markus</dc:contributor> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

Analyzing_High_Dimensional_Data_by_Subspace_Validity.pdf 99

Das Dokument erscheint in:

deposit-license Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: deposit-license

KOPS Suche


Stöbern

Mein Benutzerkonto