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Outliers — finding and classifying which genuine and which spurious

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Summary

The paper presents our experience with identifying and verifying outlying data points.

Firstly we recall the grand tour method implemented in a dynamic graphics environment and endowed with dynamically changing concentration ellipses and count plots — as proposed by Bartkowiak & Szustalewicz (1997). The method permits to select and identify some data points as suspected outliers. Next we propose to carry out a sort of classification of the found outliers by performing cluster analysis based on angular similarities of the suspected outliers. The procedure returns bundles of data vectors similar with respect to their outlyingness.

The considerations are illustrated with the Milk container data, analyzed formerly, a.o. by Atkinson (1994) and Muruzábal and Muñoz (1997).

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Authors and Affiliations

  1. Institute of Computer Science, University of Wrocław, Przesmyckiego 20, PL-51-151, Wrocław, Poland

    Anna Bartkowiak & Adam Szustalewicz

Authors
  1. Anna Bartkowiak
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  2. Adam Szustalewicz
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Additional information

Supported in part by the Polish Research Committee KBN grant no. 8 T11 C031 16

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Bartkowiak, A., Szustalewicz, A. Outliers — finding and classifying which genuine and which spurious. Computational Statistics 15, 3–12 (2000). https://doi.org/10.1007/s001800050031

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  • DOI: https://doi.org/10.1007/s001800050031

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