UPGMA clustering revisited: A weight-driven approach to transitive approximation Original Research Article

A new algorithm is proposed for generating min-transitive approximations of a given similarity matrix (i.e. a symmetric matrix with elements in the unit interval and diagonal elements equal to one). Different approximations are generated depending on the choice of an aggregation operator that plays a central role in the algorithm. If the maximum operator is chosen, then the approximation coincides with the min-transitive closure of the given similarity matrix. In case of the arithmetic mean, a transitive approximation is generated which is, on the average, as close to the given similarity matrix as the approximation generated by the UPGMA hierarchical clustering algorithm. The new algorithm also allows to generate approximations in a purely ordinal setting. As this new approach is weight-driven, the partition tree associated to the corresponding min-transitive approximation can be built layer by layer. Numerical tests carried out on synthetic data are used for comparing different approximations generated by the new algorithm with certain approximations obtained by classical methods.