Pleiades: Subspace Clustering and Evaluation

  • Ira Assent
  • Emmanuel Müller
  • Ralph Krieger
  • Timm Jansen
  • Thomas Seidl
Conference paper

DOI: 10.1007/978-3-540-87481-2_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5212)
Cite this paper as:
Assent I., Müller E., Krieger R., Jansen T., Seidl T. (2008) Pleiades: Subspace Clustering and Evaluation. In: Daelemans W., Goethals B., Morik K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science, vol 5212. Springer, Berlin, Heidelberg

Abstract

Subspace clustering mines the clusters present in locally relevant subsets of the attributes. In the literature, several approaches have been suggested along with different measures for quality assessment.

Pleiades provides the means for easy comparison and evaluation of different subspace clustering approaches, along with several quality measures specific for subspace clustering as well as extensibility to further application areas and algorithms. It extends the popular WEKA mining tools, allowing for contrasting results with existing algorithms and data sets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ira Assent
    • 1
  • Emmanuel Müller
    • 1
  • Ralph Krieger
    • 1
  • Timm Jansen
    • 1
  • Thomas Seidl
    • 1
  1. 1.Data management and exploration groupRWTH Aachen UniversityGermany

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