A discretization algorithm based on information distance criterion and ant colony optimization algorithm for knowledge extracting on industrial database
Discretization algorithms have played an important role in data mining, which is widely applied in industrial control. Since the current discretization methods can not accurately reflect the degree of the class-attribute interdependency of the industrial database, a new discretization algorithm, which is based on information distance criterion and ant colony optimization algorithm(ACO), is proposed. The paper analyses the information measures of the interdependence between two discrete variables, and an improved information distance criterion is generated to evaluate the class-attribute interdependency of the discretization scheme. In the algorithm, The ACO is applied to detect the optimal discretization scheme, and a new pheromone matrix is defined on the construction of the optimization, and an effective heuristic values assignment approach, which is used with the criterion values of discretization scheme, is proposed. We performed the experiments on a real industrial database. Experiment results verify that the proposed algorithm can produce a better discretization results.