Title: Consumer behaviour data mining of social e-commerce platform based on improved spectral clustering algorithm

Authors: Ru Zhang

Addresses: Changzhou Vocational Institute of Textile and Garment, Jiangsu Changzhou 213014, China

Abstract: In order to overcome the problems of low recall rate, precision rate and long mining time of traditional methods, a consumer behaviour data mining of social e-commerce platform based on improved spectral clustering algorithm is proposed. Firstly, the collaborative filtering algorithm is used to predict the degree of user preference, and data crawler is designed according to the LDA theme model to crawl the consumer behaviour data of social e-commerce platform, the data is cleaned and processed. Then, the initial clustering centre optimisation algorithm is used to improve the spectral clustering algorithm, and the improved spectral clustering algorithm is used to cluster the data cleaning results to realise consumer behaviour data mining. Finally, the simulation experiment proves that the recall rate and precision rate of the proposed method are both high, and the data mining time is always less than 0.55 s.

Keywords: improved spectral clustering algorithm; social e-commerce platform; consumer behaviour; data mining; LDA topic model; data crawler.

DOI: 10.1504/IJWBC.2023.134862

International Journal of Web Based Communities, 2023 Vol.19 No.4, pp.291 - 304

Received: 22 Dec 2021
Accepted: 06 May 2022
Published online: 15 Nov 2023 *

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