Abstract
A technique for analytically estimating a relaxed solution for k-means clustering was proposed based on a PCA-guided manner. In the technique, however, the derived cluster indicator is a rotated solution, in which the rotation matrix cannot be explicitly estimated. Then, such an approach as visualization by ordering of samples in connectivity matrices is applied for visually access the cluster structures. This paper introduces a technique estimating rotation matrix by Procrustean transformation of principal component scores in order to select the optimal solution from multiple solutions derived by k-Means, and proposes a cluster validation method considering the deviation between k-Means solution and re-constructed membership indicator matrix.
