Title: Study on mining repeated purchase behaviour intention of online consumers based on big data clustering

Authors: Kai Niu

Addresses: School of Digital Commerce, Beijing Information Technology College, Beijing, 100018, China

Abstract: In order to improve the performance of traditional repeat purchase behaviour intention mining methods in mining accuracy, a repeat purchase behaviour intention mining method of online consumer users based on big data clustering is proposed. Users' repeated purchase behaviour can be combined to reduce the dimension of network data. Extract the characteristics of repeated purchase behaviour of online consumers, judge the similarity of repeated purchase behaviour intention data of online consumers, establish a behaviour association rule mining model, and obtain the mining results of repeated purchase behaviour intention of online consumers. The simulation results show that the proposed method has high accuracy and short time to mine the repeated purchase behaviour intention of online consumers. The highest intention mining accuracy of this method can reach 99.99%.

Keywords: big data clustering; network consumer; repeat purchase; behavioural intention; association rules.

DOI: 10.1504/IJWBC.2024.136659

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.2 - 14

Received: 24 Feb 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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