This paper presents a method of cluster analysis based on a pseudo F-statistic (PFS) criterion function. It is designed to subdivide an ensemble into an optimal set of groups, where the number of groups is not specified and no ad hoc parameters are employed. Univariate and multivariate F-statistic and pseudo F-statistic consistency is displayed. Algorithms for feasible application of PFS are given. Results from simulations are utilized to demonstrate the capabilities of the PFS clustering method and to provide a comparative guide for other users.