In the multivariate statistics community, it is commonly acknowledged that among the hierarchical clustering tree (HCT) procedures, the single linkage rule for inter-cluster distance, tends to produce trees which are significantly more asymmetric than those obtained using other rules such as complete linkage, for instance. We consider the use of Shannon's entropy of the partitions determined by HCTs as a measure of the asymmetry of the clustering trees. On a different direction, our simulations show an unexpected relationship between Shannon's entropy of partitions and dimension of the data. Based on this observation a procedure for intrinsic dimension identification based on entropy of partitions is proposed and studied. A theoretical result is established for the dimension identification method stating that, locally, for continuous data on a d-dimensional manifold, the entropy of partitions behaves as if the local data were uniformly sampled from the unit ball of Rd. Evaluation on simulated examples shows that the method proposed compares favorably with other procedures for dimension identification available in the literature.