High-density information security storage method of big data center based on fuzzy clustering

摘要

The amounts of digital data, when it is generated for each generation, valuable information called big data, have been retained. The cluster is typically used as a research technique; this practical information mining is the process. A considerable amount of diagnosis in the context of big data is established to measure the clustering processing for big data analysis. The so-called fuzzy mechanism-only framework assembled in the security storage sector may include access to the sub-iterative method. The algorithm, based on the incentive of the design and implementation of its low computational needs fuzzy clustering algorithm, big data is possible to cluster the vast data set and biased. Handle the Random Data Storing with Optimization Fuzzy Logic algorithm (RDS-FLA) proposes random data security storage and optimization be applied to the cluster data, the fuzzy logic algorithm. Some of the large-scale data set of experimental learning data has been shown. To evaluate the vague and random data security storage and the time, the attempted performance of RDS-FLA is a form of recommendation for the execution of scalability on a big data cluster. The calculations, the complexity of time and space, run the time, cluster quality, RDS-FLA is, without affecting the quality of clustering, it is about measures in the face to show that that can be performed in a short period. Therefore, the proposed algorithm, shortening the processing time, increase the efficiently stored data security. Advantages such as optimization and efficiency of such data security costs can be determined from the algorithm?s experimental results.