Data Mining And Data Fusion Combination Of Information Processing Technology

With the rapid development of information globalization, data mining and data fusion receive great attention as two promising technologies to deal with huge volume of data. Although the principles of both are different, they can complement each other. Integrating both technologies can efficiently solve practical engineering problem. The chief problem of data mining is to search data. The fusion system could efficiently synthesize the data from different samples. Data fusion is applied on virtual model, while data mining technology can automatically establish the model. Therefore, Once highly coupled they will be helpful to accomplish complicated data analysis automatically with effect.The dissertation researches the theories of data mining and data fusion. The hybrid system's basic model is presented. The main work and achievements are as follows:(1) In order to deal with redundancy data, a new data fusion model based on rough sets is presented. Applying reduction method, the fusion algorithm is extracted. This fusion model could deal with continuous data , by using the discretization algorithm . Simulation results its feasibility.(2) One kind of fuzzy neural network, which is used to fuse multi-source data, is proposed by combing the advantages of neural network and fuzzy inference. Learning algorithm is also presented. Simulation results its feasibility.(3) Data mining and data fusion have complementary functions. According to their characteristics, the theory of integrated data mining and data fusion system is proposed.(4) A new fuzzy neural network based on rough sets is presented. Rules are extracted from database using rough sets to initialize the structure of fuzzy neural network. The fuzzy neural network is trained with original data. By combining those advantages of the two theories, the result is more satisfactory.(5) One kind of integrated data mining and data fusion system's model is designed with fuzzy neural network based on rough sets. A principle diagram and flow chart of the integrated system are presented. The model of the network is constructed. Algorithm of this model is also studied. The simulation results prove that the proposed model showsgood performance in the learning speed and accuracy.