Title: An enterprise operation management method based on mobile edge computing and data mining

Authors: Mingzhao Liu; Lei Wei

Addresses: School of Management, University of Science and Technology of China, Hefei 230000, Anhui, China ' School of Translation and Interpretation, The Hang Seng University of Hong Kong, Hong Kong 999077, Hong Kong

Abstract: E-commerce data, as an important part of enterprise online marketing data, can help enterprises understand customer needs and improve sales efficiency. Aiming at the problems of low mining efficiency, slow periodic convergence and high redundancy of traditional data mining technology, this paper constructs an e-commerce data mining method oriented to edge computing. Firstly, a fuzzy clustering algorithm is applied to e-commerce data mining, where the fuzzy partition matrix and clustering centre are obtained by optimising the objective function, and the membership function and clustering centre are repeatedly updated to a fixed range, to obtain different types of e-commerce data mining results. Secondly, Rpack edge computing deployment algorithm is adopted to build a more efficient network architecture for e-commerce data mining. Finally, the experimental results show that the average mining accuracy is higher than 99.5% and the average running time is less than 60 ms when the algorithm is applied to e-commerce data mining, which can provide decision-making basis for e-commerce enterprises. In addition, the deployment of edge computing can provide a good method for enterprise operation management.

Keywords: network marketing; edge computing; e-commerce data; data mining; enterprise operation management.

DOI: 10.1504/IJDMB.2024.137746

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.2, pp.127 - 139

Received: 12 May 2023
Accepted: 07 Sep 2023
Published online: 04 Apr 2024 *

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