Title: A feature mining model for financial product purchase behaviour data based on consumer behaviour perspective

Authors: Huijun Wang

Addresses: Department of Finance, Harbin Finance University, Harbin, 150030, China

Abstract: To overcome the problems of low accuracy, poor precision, and poor recall caused by traditional purchasing behaviour data feature mining models, the paper proposes a financial product purchasing behaviour data feature mining model based on consumer behaviour perspective. Firstly, extract financial product purchase behaviour data, preprocess and clean it to obtain the dataset to be mined. Then, based on consumer purchasing behaviour, a consumer behaviour preference function is constructed, and the regression function is used to perform secondary processing on the analysis results. Finally, the TF-IDF algorithm is applied to extract consumer behaviour vectors, and the K-means clustering analysis method is combined to accurately mine the characteristics of purchasing behaviour data. The experimental results show that the recall rate of this method can reach 98.74%, the precision rate can reach 96.51%, and the mining accuracy rate can reach 98.65%. The mining results obtained have high reliability.

Keywords: consumer behaviour analysis; data feature mining; TF-IDF algorithm; k-means cluster analysis.

DOI: 10.1504/IJBIDM.2025.143935

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.1/2, pp.205 - 217

Received: 13 Nov 2023
Accepted: 03 Jul 2024
Published online: 14 Jan 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article