Reducing Cloud Storage Space using Sematic Relational Pairwise Partition Clustering based on Frequent Correlational Document Measure in Distributed Cloud Environment - K. Thamizhchelvi and Dr.Y. Kalpana (International Journal of Advanced Science and Technology, 2020)
New arise of cloud, the complexity of data is processed in most cloud storage systems. Not only does it reduce storage space, but it also ensures the maximum amount of potential accessibility, and once the archive is allocated to a shared file system, urgent issues need to be resolved. Data space Access from Server uses storage primarily when needed in a file to collect high degrees of similarity in space capacity. To propose a new implementation for reducing the cloud storage using sematic relational pairwise partition clustering ((SRP2) based on frequent correlational document measure (FCDM) in distributed cloud environment. This frame work find the relation of summarization of key terms. It allows us to calculate the similarity of a single document, multiplication, and internal preparation. The pairwise compatibility for large document collections based on the large class of reference resolution reduce document duplication with this set and cross check the document is common, as the weights of the document's unity metrics can be represented as a paired document object. The test results, in our method of calculating all pairs of document correlation, will reduce the number of candidate document pairs compared to that frequent analysis, since the execution time and space are minimized even when compared to the existing algorithms, Keywords: document clustering, semantic relation, partition, cloud storage, data redundancy, pairwise, frequent measure