Analyzing High-Dimensional Data by Subspace Validity

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Analyzing High-Dimensional Data by Subspace Validity

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Amihood Amir Articles by Reuven Kashi Articles by Nathan S. Netanyahu Articles by Daniel Keim Articles by Markus Wawryniuk

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Amihood Amir, Reuven Kashi, Nathan S. Netanyahu, Daniel Keim, Markus Wawryniuk, "Analyzing High-Dimensional Data by Subspace Validity," Data Mining, IEEE International Conference on, pp. 473, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250955, author = {Amihood Amir and Reuven Kashi and Nathan S. Netanyahu and Daniel Keim and Markus Wawryniuk}, title = {Analyzing High-Dimensional Data by Subspace Validity}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {473}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250955}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Analyzing High-Dimensional Data by Subspace Validity SN - 0-7695-1978-4 SP EP A1 - Amihood Amir, A1 - Reuven Kashi, A1 - Nathan S. Netanyahu, A1 - Daniel Keim, A1 - Markus Wawryniuk, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Amihood Amir, Bar-Ilan University

Reuven Kashi, Bar-Ilan University

Nathan S. Netanyahu, Bar-Ilan University

Daniel Keim, University of Konstanz

Markus Wawryniuk, University of Konstanz

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250955

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.

Citation:

Amihood Amir, Reuven Kashi, Nathan S. Netanyahu, Daniel Keim, Markus Wawryniuk, "Analyzing High-Dimensional Data by Subspace Validity," icdm, pp.473, Third IEEE International Conference on Data Mining (ICDM'03), 2003

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.