Using Generalized Distances in Classification of Groups

With a set of nearly 12,000 biometrical data, comprising populations of 14 different species of clover, four methods to cluster those populations are tried, in order to compare their results and to see whether the numerical classification obtained agrees with the botanical taxonomy.

Abstract

Abstract The Mahalanobis generalized distance can advantageously be used to achieve the hierarchical clustering of groups of individuals. With a set of nearly 12,000 biometrical data, comprising populations of 14 different species of clover, we tried four methods to cluster those populations, in order to compare their results and to see whether the numerical classification obtained agrees with the botanical taxonomy. One of those methods is a conventional hierarchical clustering technique, based upon the Euclidean distances between the means of the populations, while the three other methods make use, with an increasing degree of complexity, of the generalized distances. These methods gave obviously better results.