Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach
This addresses the challenge of modeling complex real-world phenomena with cellular automata for researchers in computational modeling and data mining, though it appears incremental as it applies existing rough sets techniques to CA.
The paper tackles the problem of automatically selecting neighborhoods and determining update rules for cellular automata (CA) by proposing a method using rough sets theory for data mining, which successfully identifies both deterministic and probabilistic CA-based models of real-world phenomena from synthetic and real-world data.
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.