摘要
为了提高电子商务群体用户访问数据的挖掘能力,提出基于粗糙集的电子商务群体用户访问数据挖掘方法.构建电子商务群体用户访问数据的信息跟踪融合模型,结合模糊度特征分析方法实现电子商务群体用户访问数据的特征重构,提取电子商务群体用户访问数据的关联特征量,建立稀疏化的电子商务群体用户访问数据分布式融合模型,通过空间分块聚类分析方法建立电子商务群体用户访问数据的粗糙集匹配特征分布模型,采用空间欠采样的方法实现电子商务群体用户访问数据的优化聚类和信息融合分析,根据信息融合结果实现数据挖掘优化.仿真结果表明,采用该方法对电子商务群体用户访问数据挖掘的输出聚类性较高,特征辨识能力较好,提高数据挖掘的精度.
In order to improve the efficiency of data mining for users of E-commerce groups,mining data for users of E-commerce groups is proposed.A method of mining access data of E-commerce group users based on rough set is proposed.At first,construct an information tracking fusion model of E-commerce group user access data,which combines the ambiguity feature analysis method to realize the reconstruction of the characteristics of E-commerce group user access data Secondly,the associated feature quantity of E-commerce group users′access data is extracted,and a sparse model fusion model of distributed fusion of E-commerce group users′access data is established.Thirdly,a rough set matching feature distribution model of E-commerce group users′access data is established by spatial block clustering analysis method,and spatial under-sampling method is used to achieve optimized clustering and information fusion analysis of E-commerce group user access data.The fusion result realizes the optimization of the mining method to access data of users of E-commerce groups.The simulation results show that the output clustering of data mining for E-commerce group users is higher,the feature recognition ability is better,and the accuracy of data mining is improved.
作者
宋晓姣
胡媛媛
SONG Xiaojiao;HU Yuanyuan(School of Information Engineering and Media,Hefei vocational and Technical College,Hefei Anhui 230000)
出处
《宁夏师范学院学报》
2021年第1期55-60,共6页
Journal of Ningxia Normal University
