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High-Dimensional Outlier Detection: The Subspace Method

  • Charu C. Aggarwal
Chapter

Abstract

Many real data sets are very high dimensional. In some scenarios, real data sets may contain hundreds or thousands of dimensions. With increasing dimensionality, many of the conventional outlier detection methods do not work very effectively. This is an artifact of the well known curse of dimensionality. In high-dimensional space, the data becomes sparse, and the true outliers become masked by the noise effects of multiple dimensions, when analyzed in full dimensionality.

Keywords

Outlier Detection Outlier Analysis Subspace Analysis Outlier Score Local Subspace 
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 Science+Business Media New York 2013

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterNew YorkUSA

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