Local Subspace Based Outlier Detection

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


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.


Outlier detection Local subspace 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ankur Agrawal
    • 1
  1. 1.G.L.A. Institute of Technology and Management, MathuraMathuraIndia

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