Classification of Dataset Using Deep Belief Networks Clustering Method

Abstract

Dataset in large collection involves considerable handling in its analysis especially when it is being employed in classification problems that involve big data. Due to the technology development, the manner and approach in which this dataset is being manipulated for classification purposes differ not only in one respect but in many respects with different uncorrelated results which sometimes make prediction inaccurate. By definition, classification is the act of arranging objects into classes or categories of the same type; these objects can be huge or otherwise, and to manually classify them will be a herculean task. The basic reason for classification is to punctiliously predict the class for each case in the dataset using class label. Notable classification, clustering and regression methods are support vector machines, neural networks, random forest, k-nearest neighbor and decision trees. The conventional clustering method that is widely employed to classification problems cannot handle the weight associated problem which characterized the transmission of neurons from layer to layer within the network. Employed in this work for the classification and clustering resolution is deep belief networks clustering method. The neural network architecture and loss function popularly employ in deep learning are considered for transforming the input data to clustering-friendly feature representation. © 2020, World Academy of Research in Science and Engineering. All rights reserved.