基于双层聚类方法的网页推荐模型  

Scheme of web page usage prediction based on two-layer clustering method

作　　者：吴瑞[1] 

机构地区：[1]山西师范大学数学与计算机学院,山西临汾041004

出　　处：《系统工程学报》2013年第2期265-270,共6页Journal of Systems Engineering

基　　金：国家自然科学基金资助项目(70802043);山西省自然科学基金资助项目(2008011029-2)

摘　　要：研究web用户访问模式的聚类问题,提出了双层的用户访问模式的聚类方法.第一层采用简单易实现的LVQ(学习向量量化)神经网络方法对日志中的用户访问模式进行简单聚类,在第二层的聚类中,采用加权的模糊c-均值的方法对第一层的聚类结果进行聚类.最后根据聚类结果产生描述该类用户行为的加权访问模式,并以此作为网页推荐依据.实验结果验证了该算法的有效性和可行性.Clustering web users＇ access patterns is discussed, and a two-layer clustering approach of user access patterns is proposed in this paper. At the first layer, the learning vector quantization （LVQ） approach is exploited to group the patterns from web logs into some clusters. At the second layer, the weighted fuzzy c-means approach is developed to deal with the clustering results of the first layer. Finally weighted access patterns are created to describe the surfing behaviors of web users in the class. Then the scheme of predicting web page usage could be built. The effectiveness and feasibility of the approach are testified by the experiment results.

关 键 词：WEB挖掘 WEB聚类 用户访问模式 模糊C-均值 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]