摘要
对用户兴趣进行聚类分析对研究消费心理有着重要的意义.提出一种考虑用户兴趣分类优化的聚类模型,采用ID3决策树算法提高用户兴趣分类计算速度,将最高信息增益的属性当成前节点的检测属性,确保结果分解中的用户兴趣样本分类所需的信息量最小,构建用户兴趣分类优化的自适应模糊聚类目标函数,更新聚类原型矩阵,在自适应模糊聚类模型下,直接给出聚类原型的迭代等式,保证分类准确.实验结果说明,所提模型相对于传统聚类模型不容易陷入局部最优解,具有较高的查全率和查准率,对进一步用户行为研究有着较大的意义.
Clustering analysis was carried out on the user＆#39;s interests is of great significance to the study of consumer psychology .Considering user＆#39;s interests is a kind of classification optimization clustering model ,improve the user＆#39;s interests using the algorithm of ID3 decision tree classification calculation speed , the attribute of the highest information gain as the test attributes of nodes before ,to ensure the result of decomposition users interested in samples required minimum amount of information ,building user interest classification optimization of adaptive fuzzy clustering objective function , the update matrix clustering prototype , under adaptive fuzzy clustering model , clustering prototype iterative equation is given directly ,guarantee the accuracy of the classification .Experiment result shows that the proposed model is compared with traditional clustering model is not easy to fall into local optimal solution ,has higher recall ratio and precision ,and has great significance for further user behavior research .
出处
《微电子学与计算机》
CSCD
北大核心
2014年第5期171-174,共4页
Microelectronics & Computer
基金
河南省重点科技攻关计划项目(132102210442)
关键词
用户兴趣
分类
优化
聚类模型
仿真
user interest
classification
optimization
clustering model
simulation
