A Novel Approach to Design Distribution Preserving Framework for Big Data

Abstract

In several fields like financial dealing, industry, business, medicine, et cetera, Big Data (BD) has been utilized extensively, which is nothing but a collection of a huge amount of data. However, it is highly complicated along with time-consuming to process a massive amount of data. Thus, to design the Distribution Preserving Framework for BD, a novel methodology has been proposed utilizing Manhattan Distance (MD)-centered Partition Around Medoid (MD-PAM) along with Conjugate Gradient Artificial Neural Network (CG-ANN), which undergoes various steps to reduce the complications of BD. Firstly, the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function; subsequently, the missing data are handled by substituting or by ignoring the missed values. After that, the data are transmuted into a normalized form. Next, to enhance the classification performance, the data's dimensionalities are minimized by employing Gaussian Kernel (GK)-Fisher Discriminant Analysis (GK-FDA). Afterwards, the processed data is submitted to the partitioning phase after transmuting it into a structured format. In the partition phase, by utilizing the MD-PAM, the data are partitioned along with grouped into a cluster. Lastly, by employing CG-ANN, the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user. To analogize the outcomes of the CG-ANN with the prevailing methodologies, the NSL-KDD openly accessible datasets are utilized. The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN. The proposed work outperforms well in terms of accuracy, sensitivity and specificity than the existing systems.