Pseudo-Cellular Automata

Snippet from Paper Introduction:

One technique that is not commonly used for data classification is cellular automata. Cellular automata simulations could be used to “grow” classification regions, which could prove helpful when data can only be separated into categories using complicated, nonlinear boundaries. A simple form of using cellular automation as a data mining tool was introduced in a paper by Fawcett [3] in which local decisions are shown to viably define overall classification for specific two-dimensional datasets. However, the design of this cellular automation is kept simple, which can lead to inaccuracies when there is not a very clear separation between datapoints that belong to different classes.
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In this paper, a pseudo-cellular automation approach for classifying data is purposed which seeks to bridge the gap between classifier accuracy, practical implementation and complexity of theory.