摘要
基于机器学习算法对某大型百货商场的会员信息数据、销售流水数据、会员消费数据、商品信息数据进行分析挖掘,建立了主成分分析模型、聚类分析模型和关联规则模型。通过分析商场会员的消费特征,刻画了会员消费特征的数学模型,挖掘出其关联规则信息,并分析了连续几年该商场会员的消费趋势情况,从而为商场制定个性化的营销策略提供决策支持。
In view of the given member data,commodity data,sales flow data and member consumption data of a large department store were analyzed based on machine learning algorithm to data mining,which established the principal component analysis model,cluster analysis model and association rules model.By analyzing the consumption characteristics of shopping mall members,this paper described the mathematical model of the consumption characteristics of shopping mall members,excavated the information of association rules,and analyzed the consumption trend of the shopping mall members in successive years,thus providing decision support for the shopping mall to make personalized marketing strategies.
作者
李长生
刘宗成
张克功
LI Chang-sheng;LIU Zong-cheng;ZHANG Ke-gong(Information Technology and Educational Center,Lanzhou Petrochemical Polytechnic,Lanzhou 730060,China)
出处
《兰州石化职业技术学院学报》
2019年第4期27-31,共5页
Journal of Lanzhou Petrochemical Polytechnic
基金
工信行指委委托科研项目(GS-2019-09-20)
兰州石化职业技术学院教科研项目(JY2017-38)
兰州石化职业技术学院教研基金项目(JY2016-16)
关键词
特征选择
主成分分析
会员消费数据
聚类分析
feature selection
principal component analysis
member consumption data
clustering analysis
