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Finding non-Redundant, Statistically Significant Regions in High Dimensional Data: a Novel Approach to Projected and Subspace Clustering
Download2008
Moise, Gabriela, Sander, Joerg
Technical report TR08-03. Projected and subspace clustering algorithms search for clusters of objects in subsets of attributes. Projected clustering computes several disjoint clusters, plus outliers, so that each cluster exists in its own subset of attributes. Subspace clustering enumerates...
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High-dimensional data mining: subspace clustering, outlier detection and applications to classification
DownloadSpring 2010
Data mining in high dimensionality almost inevitably faces the consequences of increasing sparsity and declining differentiation between points. This is problematic because we usually exploit these differences for approaches such as clustering and outlier detection. In addition, the exponentially...
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