In the field of clustering techniques, little attention has been paid to the recovery of a set of clusters from the structure of an additive tree. To bridge this gap, this work presents an original partitioning technique which aims to reveal clusters from an additive tree that represents a large set of objects. Specifically, an algorithm that splits a tree into successive subtrees was developed, based on a ratio of the lengths of edges. The stability of the clusters obtained with this technique was then evaluated using measurements of cohesion and isolation that were generated using a bootstrapping strategy. Finally, the degree of association of each object to clusters was analyzed to gain insight into their internal structure. This analysis was performed on the results of a sorting task conducted by 156 subjects, who were asked to sort 96 terms associated with the odor of wine. The methodology developed in this paper represents an innovative way to highlight groups of terms within a large set of wine odor attributes, with the ultimate goal being to improve the structure of the lexicon.