Title: Data security mining method of logistics supply chain based on degree of membership conversion

Authors: Qihui Zhang; Ke Chen

Addresses: Department of Logistics and Supply Chain Management, Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, China ' Department of Management Engineering and E-commerce, Zhejiang Gongshang University, Hangzhou, Zhejiang, China

Abstract: To overcome the problems of low mining accuracy and high adjustment random error in traditional methods, this paper proposes a data security mining method for logistics supply chain based on degree of membership conversion. Analyse the logistics supply chain operation process, obtain the logistics supply chain data and establish the index system using the grey relational advantage analysis method; it also conducts supply chain data filling and exception elimination. The membership degree conversion algorithm is used to mine hidden data knowledge information, and the decentralised filtering is used to hide knowledge information. The mined membership degree value is converted into the target data mining result to realise the supply chain data security mining. The experiment shows that the adjusted random error of this method is less than 0.3, which indicates that the precision of data security mining is high.

Keywords: degree of membership conversion; logistics supply chain; data security mining; decentralised filtering; data filling.

DOI: 10.1504/IJDMB.2022.130339

International Journal of Data Mining and Bioinformatics, 2022 Vol.27 No.1/2/3, pp.27 - 44

Received: 27 Sep 2022
Accepted: 15 Dec 2022
Published online: 17 Apr 2023 *

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