A Clustering Algorithm for Cross-border E-commerce Customer Segmentation

Abstract

With the deepening of reform and opening up, cross-border e-commerce has made great progress and plays a very important role in today's society. Cross-border e-commerce is not only a place for commodity trading, but also a key channel for information communication when commodities are traded. Clustering analysis is one of the common technologies in the field of data mining, and it has its unique advantages in the application of customer segmentation. Firstly, this paper improves the selection of the initial clustering center of K-means clustering algorithm. Aiming at the defects of the existing literature, such as long time-consuming algorithm and poor accuracy when calculating the corresponding sample points for multiple maximum density parameter values as the initial clustering center, an improved scheme based on quadratic density is proposed and applied to customer value segmentation. The research shows that the improved K-means clustering algorithm significantly improves the quality of clustering, thus improving the effectiveness and pertinence of cross-border e-commerce marketing activities.