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A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

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

  • Emmanuel Müller, Karlsruher Institut für Technologie, Germany
  • Ira Assent
  • Stephan Günnemann, RWTH Aachen, Germany
  • Patrick Gerwert, RWTH Aachen, Germany
  • Matthias Hannen, RWTH Aachen, Germany
  • Timm Jansen, RWTH Aachen, Germany
  • Thomas Seidl, RWTH Aachen, Germany
  • Department of Computer Science
In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few comparative studies on the advantages and disadvantages of the different algorithms exist. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this work, we discuss the requirements for open source evaluation software and propose OpenSubspace that meets these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in clustering in high dimensional databases.
Original languageEnglish
Title of host publicationProceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011)
EditorsTheo Härder, Wolfgang Lehner, Bernhard Mitschang, Harald Schöning, Holger Schwarz
Number of pages20
PublisherGesellschaft für Informatik e.V.
Publication year2011
ISBN (print)978-3-88579-274-1
Publication statusPublished - 2011
Event14th BTW conference on Database Systems for Business, Technology and Web - Kaiserslautern, Germany
Duration: 2 Mar 20114 Mar 2011


Conference14th BTW conference on Database Systems for Business, Technology and Web

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