Abstract
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 Muruzabal and Munoz (1997).
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