Recently, big data applications have been rapidly expanded into various industries. Healthcare is one of the industries that are seek to use big data platforms and mining. As a result, some large data analytics tools have been adopted in this field. Medical imaging, which is a pillar in diagnostic healthcare, involves a high volume of data collection and processing. The most challenging issue is common in sub-graph mining process to reduce the dimensionality of medical data set is minimized. In this paper, we propose a Multi-Objective Sub-Linear Frequent Mining (MOSLFM) to estimate the real values of outline processing in biomedical data, which is useful for computational complexity. This is repeated to find the minimum representation of the most frequent supplemental edges to be compatible with the sub-border margin. Sub-linear and sub-graph often use the mining process candidate generation model to find the biometric data set used to reduce the process. Projecting a high efficient progressing cluster partitioning method is used to determine the identified terms frequency in the biomedical dataset, so the process is simplified using lower complexity.
Digital Object Identifier (DOI)
Elangovan, G. and Kavya, G.
"Multi-Objective Sub-Linear Frequent Mining-Based Information Prediction in Biomedical Datasets using Big Data Analytics,"
Applied Mathematics & Information Sciences: Vol. 13:
6, Article 12.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol13/iss6/12