基于社交电商平台的用户行为分析
首发时间:2020-06-22
摘要:随着近几年互联网经济以及社交化平台的蓬勃发展和兴起,社交电商群体越发活跃在大众的眼前,在中国的经济社会中占有越来越重要的位置,对社交电商这个新兴的群体进行用户行为分析对于经济社会发展来说具有重要意义。本文借助大数据挖掘技术,对社交电商的行为进行聚类分析,将改进后的Canopy算法结合K-means聚类算法运用在社交电商的功能访问数据上。实验证明,该算法能够有效地根据社交电商特征进行聚类分析,划分社交电商群体,为社交电商提供个性化服务方面提供理论依据。
关键词: 大数据挖掘 社交电商 用户行为 聚类分析 K-means?????
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User behavior analysis based on social e-commerce platform
Abstract:With the rapid development and rise of Internet economy and social platforms in recent years, social e-commerce groups are more and more active in front of the public, and occupy an increasingly important position in China\'s economic society. It is of great significance for the economic and social development to analyze the user behavior of social e-commerce, a new group. With the help of big data mining technology, this paper analyzes the behavior of social e-commerce, and applies the improved canopy algorithm and K-means clustering algorithm to the function access data of social e-commerce. The experimental results show that the algorithm can effectively cluster the social e-commerce groups according to the characteristics of social e-commerce, and provide theoretical basis for social e-commerce to provide personalized services.
Keywords: big datamining social e-commerce user behavior cluster analysis K-means
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