Data-parallel clustering algorithm based on mutual information mining of joint condition

In order to improve the reliability service ability of the cloud storage database, the data parallel clustering process is carried out, and the data parallel clustering algorithm based on the mutual information mining of the joint condition is proposed. a large data configuration structure model of a cloud environment virtual resource is constructed, data compression and characteristic reconstruction are carried out by adopting an online dictionary learning method, a regression analysis of the cloud environment virtual resource configuration data and a point cloud structure recombination are carried out in combination with a non-linear statistical sequence analysis method, the parallel characteristic scheduling of the large data of the virtual resources of the cloud environment is realized, the mutual information feature quantity of the joint condition is mined, the characteristic quantity of the mining is subjected to the characteristic filtering and the attribute set merging processing by adopting the fuzzy C-means clustering algorithm, by using the self-adaptive optimization algorithm, the automatic retrieval of the fuzzy clustering center is carried out, and the parallel clustering optimization of the large data is realized. The simulation results show that the classification performance of the large data clustering of the cloud environment virtual resources is good, the property classification and fusion capability is high, and the error rate is lower.