Outlier detection using ball descriptions with adjustable metric

Authors:
David M. J. Tax;Piotr Juszczak;Elżbieta Pękalska;Robert P. W. Duin
Affiliations:
Information and Communication Theory Group, Delft University of Technology, Delft, CD, The Netherlands;Information and Communication Theory Group, Delft University of Technology, Delft, CD, The Netherlands;School of Computer Science, University of Manchester, Manchester, United Kingdom;Information and Communication Theory Group, Delft University of Technology, Delft, CD, The Netherlands
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

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Abstract

Sometimes novel or outlier data has to be detected. The outliers may indicate some interesting rare event, or they should be disregarded because they cannot be reliably processed further. In the ideal case that the objects are represented by very good features, the genuine data forms a compact cluster and a good outlier measure is the distance to the cluster center. This paper proposes three new formulations to find a good cluster center together with an optimized ℓp-distance measure. Experiments show that for some real world datasets very good classification results are obtained and that, more specifically, the ℓ1-distance is particularly suited for datasets containing discrete feature values.