An efficient ACO-PSO-based framework for data classification and preprocessing in big data

Abstract

Big data is prominent for the systematic extraction and analysis of a huge or complex dataset. It is also helpful in the management of data as compared to the traditional data-processing mechanisms. In this paper, an efficient ant colony optimization (ACO) and particle swarm optimization (PSO)-based framework have been proposed for data classification and preprocessing in the big data environment. It shows that the content part can be collaborated and fetched for analysis from the volume and velocity integration. Then weight marking has been done through the volume and the data variety. In the end, the ranking has been done through the velocity and variety aspects of big data. Data preprocessing has been performed from weights assigned on the basis of size, content, and keywords. ACO and PSO are then applied considering different computation aspects like uniform distribution, random initialization, epochs, iterations, and time constraint in case of both minimization and maximization. The weight assignments have been done automatically and through an unbiased random mechanism. It has been done on a scale of 0�1 for all the separated data. Then simple adaptive weight (SAW) method has been applied for prioritization and ranking. The overall average classification accuracy obtained in the case of PSO-SAW is 98%, and in the case of ACO-SAW, it is 95%. PSO-SAW approach outperforms in all cases, in comparison to ACO-SAW.