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SOREX: Subspace Outlier Ranking Exploration Toolkit

  • Emmanuel Müller
  • Matthias Schiffer
  • Patrick Gerwert
  • Matthias Hannen
  • Timm Jansen
  • Thomas Seidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6323)

Abstract

Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. In recent research novel outlier ranking methods propose to focus on outliers hidden in subspace projections of the data. However, focusing only on the detection of outliers these approaches miss to provide reasons why an object should be considered as an outlier.

In this work, we propose a novel toolkit for exploration of subspace outlier rankings. To enable exploration of subspace outliers and to complete knowledge extraction we provide further descriptive information in addition to the pure detection of outliers. As wittinesses for the outlierness of an object, we provide information about the relevant projections describing the reasons for outlier properties. We provided SOREX as open source framework on our website it is easily extensible and suitable for research and educational purposes in this emerging research area.

Keywords

Outlier Detection Subspace Cluster Subspace Projection Outlier Property Descriptive Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Emmanuel Müller
    • 1
  • Matthias Schiffer
    • 1
  • Patrick Gerwert
    • 1
  • Matthias Hannen
    • 1
  • Timm Jansen
    • 1
  • Thomas Seidl
    • 1
  1. 1.Data management and data exploration groupRWTH Aachen UniversityGermany

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