TY - GEN
T1 - Analyzing high-dimensional data by subspace validity
AU - Amir, Amihood
AU - Kashi, Reuven
AU - Netanyahu, Nathan S.
AU - Keim, Daniel
AU - Wawryniuk, Markus
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78149344558&partnerID=8YFLogxK
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AN - SCOPUS:78149344558
SN - 0769519784
SN - 9780769519784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 473
EP - 476
BT - Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
T2 - 3rd IEEE International Conference on Data Mining, ICDM '03
Y2 - 19 November 2003 through 22 November 2003
ER -