Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data

@article{Mller2009RelevantSC,
  title={Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data},
  author={Emmanuel M{\"u}ller and Ira Assent and Stephan G{\"u}nnemann and Ralph Krieger and Thomas Seidl},
  journal={2009 Ninth IEEE International Conference on Data Mining},
  year={2009},
  pages={377-386}
}
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering… CONTINUE READING
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