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2008
SIAM

Robust Clustering in Arbitrarily Oriented Subspaces

9 years 25 days ago
Robust Clustering in Arbitrarily Oriented Subspaces
In this paper, we propose an efficient and effective method to find arbitrarily oriented subspace clusters by mapping the data space to a parameter space defining the set of possible arbitrarily oriented subspaces. The objective of a clustering algorithm based on this principle is to find those among all the possible subspaces, that accommodate many database objects. In contrast to existing approaches, our method can find subspace clusters of different dimensionality even if they are sparse or are intersected by other clusters within a noisy environment. A broad experimental evaluation demonstrates the robustness, efficiency and effectivity of our method.
Elke Achtert, Christian Böhm, Jörn David
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where SDM
Authors Elke Achtert, Christian Böhm, Jörn David, Peer Kröger, Arthur Zimek
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