User Browsing Data Mining Method of 

E-commerce Platform based on Genetic Algorithm

Yadong Zhu

Nanjing Engineering Branch, Jiangsu ｕｎｉｏｎ Technical Institute, Nanjing, 210000, China

Abstract: In order to shorten the processing time of massive e-commerce platform users browsing data and improve the processing speed of e-commerce platform users browsing data, it is necessary to study the data mining algorithm. Therefore, this paper proposes a k-means clustering data mining method based on genetic algorithm. Firstly, the data of browsing data of e-commerce platform users are collected, and the data features are extracted. Finally, the K-means clustering method of genetic algorithm is used to cluster the user browsing data of e-commerce platform to complete the data mining analysis. The results show that the method can effectively reduce the processing time of browsing data of e-commerce platform users, and can complete data mining in at least 2 seconds, and the accuracy is high, up to 97%, which has certain application value.

Keywords: E-commerce platform; Data acquisition; Data feature extraction; Data mining