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Local Subspace Based Outlier Detection

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Part of the Communications in Computer and Information Science book series (CCIS,volume 40)

Abstract

Existing studies in outlier detection mostly focus on detecting outliers in full feature space. But most algorithms tend to break down in high-dimensional feature spaces because classes of objects often exist in specific subspace of the original feature space. Therefore, subspace outlier detection has been recently defined. As a novel solution to tackle this problem, we propose here a local subspace based outlier detection technique, which uses different subspaces for different objects. Using this concept we adopt local density based outlier detection to cope with high-dimensional data. A broad experimental evaluation shows that this approach yields results of significantly better quality than existing algorithms.

Keywords

  • Outlier detection
  • Local subspace

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  • DOI: 10.1007/978-3-642-03547-0_15
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© 2009 Springer-Verlag Berlin Heidelberg

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Agrawal, A. (2009). Local Subspace Based Outlier Detection. In: , et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-03547-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03546-3

  • Online ISBN: 978-3-642-03547-0

  • eBook Packages: Computer ScienceComputer Science (R0)