Deep Belief Neural Network (DBNN)-Based Categorization of Uncertain Data Streams

Abstract

In the data mining era, the research field is paying attention to data stream mining, which offers a substantial influence on a variety of applications such as networking, wireless communications, education, economics, weather prediction, financial sector, and so on. Moreover, processing of this uncertain data stream faces two major challenges, which are computational difficulty and long processing time of data. Thus, to overcome this, this work proposes a technique that employs a deep belief neural network to categorize uncertain data streams. Initially, this work utilized a hybrid method that combines ensemble, grid, and density-dependent clustering approaches to acquire the local optimum value in uncertain data streams. Furthermore, for classification, a deep belief neural network (DBNN) has been used. As a result of mining, target semantics or chunks will be obtained from the classified data. The suggested technique performs well, and its effectiveness has been assessed in terms of time and accuracy. Thus, the proposed method outperforms the existing techniques.