Modern storage systems allow to capture data in its full complexity. As implication for the
data mining task of clustering multiple alternative and valid clusterings can be identified
for a single dataset. A second observation is that clustering based on all attributes in the
full-space is futile but valuable cluster patterns can be found for subsets of attributes.
This thesis contributes novel methods for detecting multiple alternative clusterings in
subspace projections of the data.
