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A Novel Approach to Design distribution preserving framework for Big Data

Intelligent Automation & Soft Computing 2023年第3期35卷 2789-2803页

作者： Mini Prince P.M.Joe Prathap Department of Information Technology St.Peter’s College of Engineering and TechnologyChennai600054TamilnaduIndia Department of Information Technology R.M.D Engineering CollegeChennai601206TamilnaduIndia

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 ***,it is highly complicated along with time-consuming to process a massive amount of ***,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 ***,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 *** that,the data are transmuted into a normalized ***,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 *** the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a ***,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the *** analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are *** experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed *** proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.

关键词： Big data artiﬁcial neural network ﬁsher discriminant analysis distribution preserving framework manhattan distance

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