Against this background, the present study aims to propose a novel method based on extended Deep learning (DL), recruiting autoencoder (AE) ensembles.
Given the big challenges of high computational complexity and lack of accurate mapping in multidimensional data sets, it is essential to provide innovative solutions for SC. As an exploratory data aَnalysis (EDA) process, spectral clustering (SC) reduces complex, multidimensional data sets to similar ones in rarer dimensions.
