Alternative Clustering in Subspace Projections

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.