in the physical, biological and social sciences are organized in a nested hierarchical structure, i.e. the elements of the system can be partitioned in clusters which in turn can be partitioned in subclusters and so on up to a certain level (Simon, 1962). The hierarchical structure of interactions among elements strongly affects the dynamics of complex systems. Therefore a quantitative description of hierarchies of the system is a key step in the modeling of complex systems (Anderson, 1972). The analysis of multivariate data provides crucial information in the investigation of a wide variety of systems. Multivariate analysis methods are designed to extract the information both on the number of main factors characterizing the dynamics of the investigated system and on the composition of the groups (clusters) in which the system is intrinsically organized. Recently physicists started to contribute to the development of new techniques to investigate multivariate data (Blatt et al., 1996; Hutt et al., 1999; Mantegna, 1999; Giada and Marsili, 2001; Kraskov et al., 2005; Tumminello et al., 2005; Tsafrir et al., 2005; Slonim, 2005). Among multivariate techniques, natural candidates for detecting the hierarchical structure of a set of data are hierarchical clustering methods (Anderberg, 1973).

The modeling of 