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Analyzing high-dimensional data by subspace validity

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5 Author(s)
Amir, A. ; Dept. of Comput. Sci., Bar-Ilan Univ., Ramat-Gan, Israel ; Kashi, R. ; Netanyahu, N.S. ; Keim, D.
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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.

Published in:

Data Mining, 2003. ICDM 2003. Third IEEE International Conference on

Date of Conference:

19-22 Nov. 2003

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