As the mobile information nodes change greatly, the mobile data is rather vague and noisy, making more dimensions for the input information in the data mining based on traditional correlation mapping. The great number of dimensions complicates the network structure, which lowers the efficiency of data mining. To improve accuracy, based on mobile information node, it sets the two-layer neural network with non-linear connection weight as the information distinguishing system, in which the relation between any two figures in two data sets would be described. The association attribute groups would be shown in the form of correlation coefficient matrix, while coefficients of difference in the form of the reciprocal of correlation coefficient matrix. Then combine neural network and rough set (RS), analysing the change of mobile information node from moving direction and distance and simplifying the sample set for neural network learning with RS. At the same time, the input and output data is normalised and the redundant data and redundant attributes deleted to get a simplified attribute set. Finally, the authors learn and train with the simplified sample set to ensure the qualified mining accuracy. The result in the simulation experiment would efficiently improve the mining accuracy and efficiency.

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Simulation research for telecommunication data mining based on mobile information node

References

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