Subspace Clustering for Uncertain Data

  title={Subspace Clustering for Uncertain Data},
  author={Stephan G{\"u}nnemann and Hardy Kremer and Thomas Seidl},
Analyzing uncertain databases is a challenge in data mining research. Usually, data mining methods rely on precise values. In scenarios where uncertain values occur, e.g. due to noisy sensor readings, these algorithms cannot deliver highquality patterns. Beside uncertainty, data mining methods face another problem: high dimensional data. For finding object groupings with locally relevant dimensions in this data, subspace clustering was introduced. For high dimensional uncertain data, however… CONTINUE READING
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Fast algorithms for projected clustering

C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, J. S. Park
In SIGMOD, • 1999
View 17 Excerpts
Highly Influenced

A Survey of Uncertain Data Algorithms and Applications

IEEE Transactions on Knowledge and Data Engineering • 2009
View 2 Excerpts

Density-Based Probabilistic Clustering of Uncertain Data

2008 International Conference on Computer Science and Software Engineering • 2008
View 1 Excerpt

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