Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data (original) (raw)

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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Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538, München, Germany
    Hans-Peter Kriegel, Peer Kröger, Erich Schubert & Arthur Zimek

Authors

  1. Hans-Peter Kriegel
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  2. Peer Kröger
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  3. Erich Schubert
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  4. Arthur Zimek
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Editor information

Editors and Affiliations

  1. Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5 Tiwanont Road, 12000, Bangkadi, Muang, Pathumthani, Thailand
    Thanaruk Theeramunkong
  2. Dept. of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 10330, Bangkok, Thailand
    Boonserm Kijsirikul
  3. Faculty of Science & Engineering, York University, 355 Lumbers Building, 4700 Keele Street, M3J 1P3, Toronto, Ontario, Canada
    Nick Cercone
  4. School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, 923-1292, Ishikawa, Japan
    Tu-Bao Ho

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© 2009 Springer-Verlag Berlin Heidelberg

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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. https://doi.org/10.1007/978-3-642-01307-2\_86

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