Classifier for Distributed Data Mining Based on Cellular Automata

Owing to low classification accuracy and high memory overhead of the normal classifier,a Pattern Classifying Machine(PCM) named tsPCM based on Multiple Attractor Cellular Automata(MACA) for Distributed Data Mining(DDM) is designed,by changing the characterization of a MACA to two stage with two linear operators of Dependency String(DS) and Dependency Vector(DV),and employing Genetic Algorithm(GA) formulation.Plentiful experimental results prove the potential of tsPCM.Its classification complexity is declined from O(n3) to O(n),and it has the respect to excellent classification accuracy and low memory overhead established the availability of the classifier to manipulate the distributed data mining.