Title: Data mining method of mobile e-commerce consumer purchase behaviour

Authors: Shuang Li

Addresses: Department of Information and Mechatronics Engineering, Hunan International Economics University, Changsha 410205, China; Department of Graduate School, Lyceum of the Philippines University, Manila Campus, Manila 1002, Philippines

Abstract: In order to improve the accuracy and efficiency of data mining of consumer purchase behaviour in mobile e-commerce, a data mining method of consumer purchase behaviour in mobile e-commerce is proposed. Firstly, through the calculation of support in principal component analysis, the data characteristics of mobile e-commerce consumers' purchase behaviour are extracted. Then, the original residual of purchase behaviour data is calculated through iterative test, and the original feature data is corrected to complete the preprocessing. Finally, the Boolean rules in association rules are used to determine the association degree between purchase behaviour data, and the minimum threshold of purchase behaviour data is calculated. By establishing the correlation function of mobile e-commerce consumer purchase behaviour data, mining the characteristic information of mobile e-commerce consumer purchase behaviour data, and completing the purchase behaviour data mining. The results show that the highest accuracy of data mining is 98.1%.

Keywords: principal component analysis; iterative test method; Boolean rule; mobile e-commerce; consumer buying behaviour.

DOI: 10.1504/IJWBC.2024.136650

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.75 - 87

Received: 22 Feb 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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