Abstract
This research proposed a new method of grouping of people after principal component analysis. First, it is shown that the usual measure of principal component scores depends on the number of variables, making it difficult to compare many results with each other. For overcoming this drawback, a renormalization of the principal component scores is proposed in two ways. One is grouping of individuality, the other is grouping of selecting specially outlier. The plots are given in a two-dimensional map, so that the nine classified regions can be given meaning. This method is an excellent way to classify all people according to their tendencies or to select extreme people based on rules. The advantage of this method is that there are no omissions in the classification and the classification is automatically calculated. The meaning of the axes is absolute and comparisons can be made in various mappings of any data with different numbers of items.
