Cluster Analysis via Density Functions: Amelioration of a Lower Bound of the Parameter
B. Kopp · Biometrical Journal · 1984
Abstract This paper refers to an earlier investigation on Cluster Analysis procedures based on general empirical density functions, in which the number of classes is controled by a positive parameter λ: the number of clusters, m, increases as λ →∞. Since there is no functional relationship between m and λ, upper and lower bounds of the parameter are of interest (see KOPP, 1976a). We now give a better value for the lower bound of λ in the case of the multivariate normal distribution.