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Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering

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Authors: Gabriela Moise University of Alberta, Edmonton, AB, Canada
Jörg Sander University of Alberta, Edmonton, AB, Canada
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· Proceeding
KDD '08 Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM New York, NY, USA ©2008
table of contents ISBN: 978-1-60558-193-4 doi>10.1145/1401890.1401956
Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering Published by ACM 2008 Article
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· Citation Count: 9


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