Title: Predictive model of consumer online purchase behaviour based on data mining

Authors: HaoJie Zi

Addresses: Department of Economics and Management, Huanghe Jiaotong University, Wuzhi, Henan Province, 454950, China

Abstract: The existing consumer online purchase behaviour prediction model does not reduce the noise of purchase behaviour data, which leads to poor prediction effect. Therefore, a consumer online purchase behaviour prediction model based on data mining is proposed in this paper. The behaviour data are divided into different datasets by K-means clustering. The neighbourhood rule is used to update the centre of clustering sample data and collect behaviour characteristic data. Empirical mode decomposition (EMD) method is used to obtain the instantaneous frequency of purchasing behaviour and denoise the characteristic data of consumers' online purchasing behaviour. With the data mining method, the system of consumer online purchasing behaviour characteristics is established, and a consumer online purchasing behaviour prediction model is built according to the behaviour characteristics fusion selection means to realise behaviour prediction. The results show that the accuracy of this model to predict consumers' online purchasing behaviour is more than 93%.

Keywords: data mining; consumer behaviour; purchasing behaviour; behaviour prediction; online shopping.

DOI: 10.1504/IJWBC.2023.128405

International Journal of Web Based Communities, 2023 Vol.19 No.1, pp.39 - 52

Received: 05 Jul 2021
Accepted: 01 Dec 2021
Published online: 20 Jan 2023 *