Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

  • Hans-Peter Kriegel
  • Peer Kröger
  • Erich Schubert
  • Arthur Zimek
Conference paper

DOI: 10.1007/978-3-642-01307-2_86

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5476)
Cite this paper as:
Kriegel HP., Kröger P., Schubert E., Zimek A. (2009) Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. In: Theeramunkong T., Kijsirikul B., Cercone N., Ho TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science, vol 5476. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hans-Peter Kriegel
    • 1
  • Peer Kröger
    • 1
  • Erich Schubert
    • 1
  • Arthur Zimek
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMünchenGermany

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