Efficient Enhancement on Cellular Automata for Data Mining

Cellular Automata (CA) is a discrete dynamical system consists of an array of identically programmed automata, or cells, which interact with one another in a neighborhood relationship and have definite state. It can be used to show how the elements of a system interact with each other. It has a complex and varied behavior whose behavior is completely specified in terms of a local relation. Represented as a uniform grid, the time advances in discrete steps and the laws are expressed in a small lockup table through which at each step each cell computes its new state from that of its neighbors. With Data Mining deal with process of clustering and classification of large amounts of data and getting new knowledge from the data this paper introduce a new cellular automata classifier model. The practical experiments showed that our model run about twice times faster than the old model with high accuracy as we will see through this paper.