Application of extended state cellular automata to spatiotemporal data mining

The paper introduces an extended state cellular automata (CA) model to spatio-temporal data mining(STDM). The core of the model adds numerable and uncountable attribute to the cell and intends to resolve the problem of the sparse data and large attribute information interaction in the spatial and spatiotemporal data mining tasks. The preliminary experiment shows the approach is suited for the nonlinear problems, even in the face of sparse data. They can tackle problems of previously prohibitive complexity and also improve previous approaches. The paper advises the method in combination with domain knowledge and other data mining techniques offer a chance to discover nonlinear spatiotemporal relationships.