Robust Clustering in Arbitrarily Oriented Subspaces

In this paper, we propose an ecient and eective method to find arbitrarily oriented subspace clusters by mapping the data space to a parameter space defin- ing 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 sub- spaces, that accommodate many database objects. In contrast to existing approaches, our method can find subspace clusters of dierent dimensionality even if they are sparse or are intersected by other clusters within a noisy environment. A broad experimental evaluation demonstrates the robustness, eciency and eectivity