We develop and implement an algorithm for the quantitative characterization
of cluster dynamics occurring on cellular automata defined on an arbitrary
structure. As a prototype for such systems we focus on the Ising model on a
finite Sierpsinski Gasket, which is known to possess a complex thermodynamic
behavior. Our algorithm requires the projection of evolving configurations into
an appropriate partition space, where an information-based metrics (Rohlin
distance) can be naturally defined and worked out in order to detect the
changing and the stable components of clusters. The analysis highlights the
existence of different temperature regimes according to the size and the rate
of change of clusters. Such regimes are, in turn, related to the correlation
length and the emerging "critical" fluctuations, in agreement with previous
thermodynamic analysis, hence providing a non-trivial geometric description of
the peculiar critical-like behavior exhibited by the system. Moreover, at high
temperatures, we highlight the existence of different time scales controlling
the evolution towards chaos.Comment: 20 pages, 8 figure