Scalable Density-Based Subspace Clustering

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

  • Emmanuel Müller, Karlsruhe Institute of Technology, Karlsruhe , Germany
  • Ira Assent
  • Stephan Günnemann, RWTH Aachen University, Germany
  • Thomas Seidl, RWTH Aachen University, Germany
  • Department of Computer Science

For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well.
Original languageEnglish
Title of host publicationProceedings of the 20th ACM international conference on Information and knowledge management
Number of pages10
PublisherAssociation for Computing Machinery
Publication year2011
ISBN (print)978-1-4503-0717-8
Publication statusPublished - 2011
Event20th ACM Conference on Information and Knowledge Management. CIKM 2011 - Glasgow, United Kingdom
Duration: 24 Oct 201128 Oct 2011


Conference20th ACM Conference on Information and Knowledge Management. CIKM 2011
LandUnited Kingdom

See relations at Aarhus University Citationformats


  • CIKM 2011

    Activity: Participating in or organising an event typesParticipation in or organisation af a conference

ID: 41951862