Incomplete big data imputation mining algorithm based on BP neural network

As the traditional big data imputation mining process is time-consuming with low efficiency, in this paper, an incomplete big data imputation mining algorithm based on improved BP neural network was proposed. The algorithm firstly integrated into BP artificial network neural algorithm to randomly ge...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2019-01, Vol.37 (4), p.4457-4466
Main Author: Liu, Yutang
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Computer simulation
Data management
Data mining
Data search
Entropy (Information theory)
Information theory
Missing data
Neural networks
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Summary:As the traditional big data imputation mining process is time-consuming with low efficiency, in this paper, an incomplete big data imputation mining algorithm based on improved BP neural network was proposed. The algorithm firstly integrated into BP artificial network neural algorithm to randomly generate the initial network weight of incomplete big data, and then trained the set of weights to design an incomplete big data gene matrix. On this basis, the global search of incomplete big data information was carried out, and the big data was divided into complete and incomplete data with the search result as the core. The concept of entropy in information theory was used to perform imputation of missing values through the attribute value of the same type of complete data information. The experimental simulation proves that the incomplete big data imputation mining algorithm based on BP neural network can realize the mining of incomplete big data and improve the imputation precision of missing data.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-179278