Alternative Clustering in Subspace Projections

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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.