2014
DOI: 10.1007/978-3-642-55195-6_68
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Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach

Abstract: 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 rulelearning 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 identif… Show more

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Cited by 2 publications
(1 citation statement)
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“…Determining mathematical and statistical rules that govern biological growth processes and their interactions with mineralization processes is nontrivial but important for many problems. Consequently, various techniques have been used, including machine learning (Richards et al , 1990; Campbell et al , 2004; Placzek, 2014; Gurikov et al , 2016), coevolution (Juille and Pollack, 1998), and histogram-based methods (Schubert et al , 2017).…”
Section: Biosignature Phenomenamentioning
confidence: 99%
“…Determining mathematical and statistical rules that govern biological growth processes and their interactions with mineralization processes is nontrivial but important for many problems. Consequently, various techniques have been used, including machine learning (Richards et al , 1990; Campbell et al , 2004; Placzek, 2014; Gurikov et al , 2016), coevolution (Juille and Pollack, 1998), and histogram-based methods (Schubert et al , 2017).…”
Section: Biosignature Phenomenamentioning
confidence: 99%