摘要
研究基于数据挖掘技术,解决互联网数据量大和数据之间不互通导致的无法准确分析用户行为特征问题,提出一种互联网用户行为特征分析系统,并应用改进的k-means算法重点对系统的数据分析模块进行设计与实现。将该系统应用于某市联通网上商城平台对其用户行为进行数据分析,可有效从网站转化率、搜索词、用户、商品四个方面实现对互联网用户行为的准确分析。为电子商务平台制定更好的网络消费服务和产品设计提供了有力的数据支撑。
Based on data mining technology,this paper proposes an Internet user behavior characteristics analysis system in order to solve the problem of inaccurate analysis of user behavior caused by the large amount and interoperability among data,and focuses on the design and implementation of the data analysis module of the system by applying the improved k-means algorithm.The system is applied in a City Unicom online mall platform,and the user behavior is analyzed.The results show that this system can effectively analyze the user behavior in four aspects of website conversion rate,search terms,users and commodities,and provide a strong data support for better network consumption services and product design of e-commerce platform.
作者
李曼
LI Man(Shangqiu Polytechnic,Shangqiu Henan 476000,China)
出处
《顺德职业技术学院学报》
2021年第4期10-14,共5页
Journal of Shunde Polytechnic
基金
商丘市哲学社会科学规划项目(SKG-2019-032)。
作者简介
李曼(1982-),女,河南商丘人,讲师,硕士,研究方向:软件工程、数据挖掘、计算机网络技术等。
