Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

Authors:
Hans-Peter Kriegel;Peer Kröger;Erich Schubert;Arthur Zimek
Affiliations:
Ludwig-Maximilians-Universität München, München, Germany 80538;Ludwig-Maximilians-Universität München, München, Germany 80538;Ludwig-Maximilians-Universität München, München, Germany 80538;Ludwig-Maximilians-Universität München, München, Germany 80538
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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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.