Robust Clustering in Arbitrarily Oriented Subspaces

@inproceedings{Achtert2008RobustCI,
  title={Robust Clustering in Arbitrarily Oriented Subspaces},
  author={Elke Achtert and Christian B{\"o}hm and J{\"o}rn David and Peer Kr{\"o}ger and Arthur Zimek},
  booktitle={SDM},
  year={2008}
}
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… CONTINUE READING
Highly Cited
This paper has 26 citations. REVIEW CITATIONS

References

Publications referenced by this paper.
Showing 1-10 of 19 references

Densityconnected subspace clustering for high-dimensional data

  • K. Kailing, H.-P. Kriegel, P. Kröger
  • Proceedings of the 4th SIAM International…
  • 2004
2 Excerpts

Similar Papers

Loading similar papers…