Automated Storage Method for Big Data in Periodical Classification Based on Group Collaboration and Intelligent Clustering

The traditional method of automating the storage of big data on periodicals cannot guarantee accuracy and rationality in the process of decomposition.A merging strategy is unreasonable, leads to deviations in the process of optimization, and greatly reduces the efficiency of the classification and storage of the periodicals.To solve this problem, we propose a new method of automating the storage based on swarm collaborative intelligence clustering.First, we determine the initial structure of a radial basis function neural network.Next, we obtaine the hidden node group by calculating the diameter of its base through a sample distribution and regarde it as the initial set.The highest classification storage accuracy, the largest F-measure, and the highest similarity of journal features are taken as objective functions, and weighted sums are used as fitness functions.In the process of solving, we combine a distribution estimation algorithm and a genetic algorithm to produce new individuals through simulated annealing and realized intelligent clustering by group cooperation.The optimal individual is obtained by evolution, and the final elite set is obtained.The periodical classification of big data is automatically stored by the obtained radial basis neural network.Experimental results show that the proposed method has strong storage performance.