Abstract
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.
Original language | American English |
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Title of host publication | Data Mining, 2003. ICDM 2003. Third IEEE International Conference on |
State | Published - 2003 |