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

TL;DR

This paper introduces a data mining method based on rough sets theory to automatically select neighborhoods and identify update rules for cellular automata, enabling modeling of complex real-world systems.

Contribution

It presents a novel approach combining rough sets and rule learning for neighborhood selection and rule induction in cellular automata.

Findings

Effective identification of deterministic CA models

Successful modeling of probabilistic CA systems

Applicable to both synthetic and real-world data

Abstract

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.