  In this paper a method is proposed which uses data mining techniques based on
rough sets theory to select neighborhood and determine update rule for cellular
automata (CA). According to the proposed approach, neighborhood is detected by
reducts calculations and a rule-learning algorithm is applied to induce a set
of decision rules that define the evolution of CA. Experiments were performed
with use of synthetic as well as real-world data sets. The results show that
the introduced method allows identification of both deterministic and
probabilistic CA-based models of real-world phenomena.
