摘要
目前商业运营还存在投入成本高但管理不够精细,店铺属性关联不明显等问题。基于此,文章利用机器学习中的聚类模型、关联规则模型解决了上述问题。特别是,利用PCA(principle component analysis,主成分分析)技术将高维数据降维,便于将高维数据的聚类结果进行可视化分析。实验结果验证了文章所提出的算法能够有效的将店铺根据其消费记录进行分类,以及能够利用关联规则将店铺进行兴趣关联和层次定位,运行结果证明该方法是正确和有效的。
At present,there are still some problems in commercial operation,such as high investment cost but not meticulous management,not obvious store attribute correlation and so on.Based on this,this paper uses the clustering model and association rule model in machine learning to solve the above problems.In particular,PCA(principal component analysis)technology is used to reduce the dimension of high-dimensional data,which is convenient for visual analysis of clustering results of highdimensional data.The experimental results show that the proposed algorithm can effectively classify stores according to their consumption records,and can use association rules to associate stores'interests and locate their levels.The running results show that the method is correct and effective.
作者
赵心夷
ZHAO Xin-yi(College of computer and Cyberspace Security,Hebei Normal University,Shijiazhuang 050000,China)
出处
《电脑与信息技术》
2022年第2期77-80,共4页
Computer and Information Technology
关键词
机器学习
聚类
降维
PCA
关联规则
machine Learning
clustering
dimensionality reduction
PCA
association rule analysis
