Incomplete Data Filling Mining Algorithm Based on the Improved Genetic Algorithm

Traditional big data high filling mining process is time-consuming, the defects of low efficiency. Based on improved genetic algorithm of incomplete data filling mining algorithm. This algorithm fusion in the BP artificial neural network algorithm first randomly generated incomplete data the initial network weights, and to train the weights of the group to design a incomplete data gene matrix, by using the genetic algorithm based on the gene matrix to calculate the incomplete data adaptive value function, and at the same time coding, hybridization and mutation genetic operation, on the basis of incomplete big data information of global search, with its search results as the core will be divided into complete and incomplete data, using the concept of entropy in information theory, and the same kind of complete data information of attribute values to fill the missing value. The experimental simulation proves that the incomplete big data based on improved genetic algorithm of incomplete filling mining method can realize data mining, improve the filling precision of the missing data.