Big Data Performance Analysis on a Hadoop Distributed File System Based on Geometric Data Perturbation Technique
V. Santhana Marichamy, V. Natarajan · Procedia Computer Science · 2019
In this paper, we proposed a Big Data Security Analysis based on a modified perturbation technique in a Hadoop Distributed File System which is commonly used in various real time applications. This work have utilized an improved K-means clustering algorithm, which selects the initial clustering centres based on the density parameters. The poor assurance of initial centres and in order to improve the precision and packing effect of the K-means clustering computation is to be need a new method. In order to group the data of same cluster based on their similarities clustering algorithm was used. In the various clustering methods, this paper is utilizing the geometric data perturbation technique which is clustered, data are perturbed whose values are challenging to be recognized. The performance was evaluated on a sample Health Care database and the metrics under studies are memory usage, precision, recall, accuracy, F-measure, clustering time and execution time, Perturbation time, De-perturbation time. In this paper, we focused only on Perturbation Time, De-Perturbation Time and the Accuracy. The experimental result shows that the proposed approach reduces the complexity and shown to less computation time and better accuracy than that of the existing techniques.