A Hierarchical Model of E-Commerce Sellers Based on Data Mining
Author(s) -
Xiuyan Bai
Publication year - 2020
Publication title -
ingénierie des systèmes d information
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.161
H-Index - 8
eISSN - 2116-7125
pISSN - 1633-1311
DOI - 10.18280/isi.250116
Subject(s) - e commerce , computer science , data mining , data science , business , world wide web
Received: 15 July 2019 Accepted: 29 October 2019 This paper attempts to accurately classify e-commerce sellers based on data mining. Firstly, the original data from an e-commerce platform were preprocessed, and the classification indices were identified from five categories (products, users, traffic, sales and basic attribute). Next, the principal component analysis (PCA) and the self-organizing feature map (SOM) were fused into a hierarchical model that divides e-commerce sellers into three categories: large sellers, medium sellers and small sellers. The effectiveness of our model was verified through experiments. Finally, several operating strategies were put forward for e-commerce sellers in each category. The research results provide a good reference for the development of the e-commerce industry.
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