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In this paper a method is proposed which uses data mining techniques based onrough sets theory to select neighborhood and determine update rule for cellularautomata (CA). According to the proposed approach, neighborhood is detected byreducts calculations and a rule-learning algorithm is applied to induce a setof decision rules that define the evolution of CA. Experiments were performedwith use of synthetic as well as real-world data sets. The results show thatthe introduced method allows identification of both deterministic andprobabilistic CA-based models of real-world phenomena.

Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach