Title: Cluster analysis-based big data mining method of e-commerce consumer behaviour

Authors: Lei Xue

Addresses: School of Economics and Management, Henan Polytechnic Institute, Nanyang, 473000, China

Abstract: In order to overcome the problems of low precision and long time of data mining in traditional big data mining methods of consumer behaviour, a clustering analysis method for big data mining of e-commerce consumer behaviour is proposed. In this paper, the K-means algorithm is used to calculate the similarity of behaviour clustering nodes of fee payers, determine the clustering process of consumer behaviour data and determine the mining weight of behaviour data. According to the FCM clustering algorithm, the target function for data mining of e-commerce consumer behaviour is constructed. According to Lagrange multiplication, the membership degree of consumer behaviour data is obtained, and the big data mining of consumer behaviour in e-commerce is realised. The experimental results show that with the method proposed in this paper, when the number of consumers is 500, the time for big data mining of consumer behaviour is 15.6s and the accuracy of behaviour big data mining is 95.34%.

Keywords: data mining; K-means clustering analysis; FCM clustering algorithm; Lagrange multiplication; cluster node.

DOI: 10.1504/IJWBC.2023.128409

International Journal of Web Based Communities, 2023 Vol.19 No.1, pp.53 - 63

Received: 21 May 2021
Accepted: 25 Nov 2021
Published online: 20 Jan 2023 *