摘要
为了解决社交网络隐式用户行为数据挖掘过程中关联相似性计算较为困难的问题,提出了基于决策树的社交网络隐式用户行为数据挖掘方法。将社交网络视为包含不同维度的向量空间,计算特定维度上用户的兴趣空间和兴趣点。确定样本属性集后,根据已知行为数据建立测试分支,计算该分支下子集的属性权重,不断迭代直至挖掘到同等属性的数据点为止。测试结果表明:该方法可对不同种类隐式用户行为精准挖掘,目标行为数据查找效果较好,实用性较强。
In order to solve the problem of social network that it is difficult to calculate the association similarity in the process of data mining for implicit user behavior,a data mining method based on decision tree for implicit user behavior in social network was proposed.Social network was regarded as a vector space containing different dimensions,and users′ interest space and interest points on specific dimensions were calculated.After determining the sample attribute set,the test branch was established according to the known behavior data,and the attribute weight of branch subset was calculated.In addition,it was iterated until the data points with the same attributes were mined.Test results show that the as-proposed method can ensure accurate mining in the face of different types of implicit user behavior,and the search for target behavior data is effective and practical.
作者
韩永印
王侠
王志晓
HAN Yongyin;WANG Xia;WANG Zhixiao(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,Jiangsu,China;School of Information Engineering,Xuzhou College of Industrial Technology,Xuzhou 221140,Jiangsu,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2024年第3期312-317,共6页
Journal of Shenyang University of Technology
基金
国家自然科学基金面上项目(61876186)
徐州市科技计划项目(KC21300)。
关键词
决策树
社交网络
隐式用户行为
向量空间
属性集
数据挖掘
权重值
属性元素
decision tree
social network
implicit user behavior
vector space
set of properties
data mining
weight value
attribute element
