The axis along which the data in n-dimensional space is primarily distributed. In two dimensions, the first principal axis is the semimajor axis of the ellipse that best fits the data set. Multiple principal axes are always orthogonal. Data are sometimes rearranged to be in principal component space before further analysis (such as cluster analysis) is performed. Analysis on data that have been transformed into principal component space is referred to as principal component analysis, or PCA.