Simulation Analysis of Network Difference Data Optimization Mining

A new data mining algorithm based on inverse distance interpolation and distributed object recognition is proposed. Building a network difference data acquisition and signal pre processing model, disturbance search clustering algorithm for network data difference distribution of the source range estimation using the inverse distance weighted interpolation, the sampling data covariance matrix to average the average time and space, based on distributed object recognition algorithm to achieve accurate mining difference data of network. Simulation results show that the proposed algorithm can effectively improve the accuracy of the distributed source estimation of network difference data, and it has higher accuracy and better performance than the traditional algorithm.