Abstract
A method of displaying an outlier in a multivariate data set is the Outlier Displaying Component. This method is based on the sample mean vector and the sum of squares and cross-product matrix. The main weakness of this method is that both of these measures involve the very outlier that is being detected. This paper presents an approach to eliminating this weakness. By eliminating the outlier from the sample mean vector and the sum of squares and cross-product matrix, the proposed method combines a number of advantages: It enhances the separation of the outlier from the rest of the data so that it appears more distinct. It also increases the general dispersion in the projected data so that the presence of multiple outliers could be revealed.
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