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

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Emmanuel Müller, RWTH Aachen University, Germany
  • Ira Assent
  • Stephan Günnemann, RWTH Aachen University, Germany
  • Ralph Krieger, RWTH Aachen University, Germany
  • Thomas Seidl, RWTH Aachen University, Germany
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 (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably achieves top clustering quality while competing approaches show greatly varying performance.
Original languageEnglish
Title of host publicationProc. IEEE International Conference on Data Mining (ICDM 2009)
Publication year2009
ISBN (print)978-0-7695-3895-2
Publication statusPublished - 2009
Externally publishedYes

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