The validation of four ultrametric clustering algorithms

Abstract

The present simulation study examined the ability of four hierarchical clustering algorithms to recover the true structure in data sets which satisfied both the ultrametric inequality and the structural model of the clustering procedures. The results indicated that the rank order performance of the four methods differed markedly from the rank order generally found in multivariate normal mixture model studies. The differing rank orders demonstrates a lack of robustness of the algorithms over alternative conceptualizations of cluster structure.