DATA STORING AND RETRIEVAL METHOD IN BIG DATA USING FUZZY BASED SCALABLE CLUSTERING ALGORITHMS

A massive volume of digital data holding valuable information, called Big Data, is produced each generation. To process and excavate such valuable information, clustering is commonly used as a data investigation technique. A huge amount of Big Data diagnostics contexts have been established to measure the clustering procedures used for big data analysis. There exists one and only framework called Fuzzy based mechanism which actually fits in for iterative method by associate in storage divisions and accessible. The proposed algorithm is motivated towards the design and implementation of fuzzy based clustering algorithms on big data, which could be present for clustering huge datasets due to their low computational necessities. In this paper, we propose Random Data Storing with Optimization Fuzzy Logic algorithm (RDS-FLA) applied on cluster data to handle the tasks that are connected with big data clustering. Experimental trainings data taking place several big datasets have been showed. The performance of RDS-FLA is tried in evaluation with the proposed scalable form of the temporal fuzzy and Random data storing that is implemented on the big data cluster. The computation outcomes are recounted in terms of time and space complexity, run time and measure of clustering quality, showing that RDS-FLA is able to run in much less time without compromising the clustering quality. Thus, the algorithm proposed alleviates the processing time and increases the security of storage data effectively. Advantages such as cost optimization and efficiency in data security can be identified from the experimental results of proposed algorithm