Advances in Knowledge Discovery and Data Mining

Volume 5476 of the series Lecture Notes in Computer Science pp 831-838

Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

  • Hans-Peter KriegelAffiliated withLudwig-Maximilians-Universität München
  • , Peer KrögerAffiliated withLudwig-Maximilians-Universität München
  • , Erich SchubertAffiliated withLudwig-Maximilians-Universität München
  • , Arthur ZimekAffiliated withLudwig-Maximilians-Universität München

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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 full-dimensional approaches and scales well to high dimensional databases.