Research on the Massive Data Classification Method in Large Scale Computer Information Management

摘要

In the process of the massive data classification in large-scale computer information management system, due to the large amount of data, and included large number of feature data, the correlation of data is reduced, resulting in low efficiency of computer operation. A model for massive data depth classification mining based on belief network is put forward. According to the relation between the probabilities of data in all the data domain, the correlation between knowledge and data domain can be inferred. Through the training sample set find the most suitable Bayesian belief network for the sample data, then according to the possible management structure and the tacit understanding degree between data samples, the optimal solution within data management structure of large scale computer. The experimental results show that, using the improved algorithm for massive data classification processing, can improve the accuracy of classification, and achieve satisfactory results.