Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

  title={Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data},
  author={Hans-Peter Kriegel and Peer Kr{\"o}ger and Erich Schubert and Arthur Zimek},
We propose an original outlier detection schema that detects outliers in varying subspaces of a high dimensional feature space. In particular, for each object in the data set, we explore the axis-parallel subspace spanned by its neighbors and determine how much the object deviates from the neighbors in this subspace. In our experiments, we show that our novel subspace outlier detection is superior to existing fulldimensional approaches and scales well to high dimensional databases. 
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What is the nearest neighbor in high dimensional spaces

  • A. Hinneburg, C. C. Aggarwal, D. A. Keim
  • Proc. VLDB
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