Publikation: Analyzing High-Dimensional Data by Subspace Validity
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2003
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Third IEEE International Conference on Data Mining. IEEE Comput. Soc, 2003, pp. 473-476. ISBN 0-7695-1978-4. Available under: doi: 10.1109/ICDM.2003.1250955
Zusammenfassung
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.
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Third IEEE International Conference on Data Mining, Melbourne, FL, USA
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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. IEEE Comput. Soc, 2003, pp. 473-476. ISBN 0-7695-1978-4. Available under: doi: 10.1109/ICDM.2003.1250955BibTex
@inproceedings{Amir2003Analy-5491,
year={2003},
doi={10.1109/ICDM.2003.1250955},
title={Analyzing High-Dimensional Data by Subspace Validity},
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}
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