基于模糊模拟的加权偏爱浏览模式的挖掘

每个网页由不同的专家给出语义上的重要性评估,这些语义评估再被刻画成相应的模糊语言变量,通过模糊模拟的方法,这些模糊语言变量被转化成表示网页重要性的权重。此外,简单地认为用户的访问频度反映了用户的访问兴趣是不准确的,因此在提出的加权支持度和偏爱度概念的基础上,从建立的包含了所有用户浏览信息的FLaAT(Frequent Link and Access Tree)上,挖掘用户偏爱的加权浏览模式。试验证明该算法是行之有效的。

Mining Weighted Preferred Browsing Patterns Based on Fuzzy Simulation

In this paper,importance evaluations of each web page are provided by different experts.These linguistic evaluations are characterized as corresponding fuzzy linguistic variables.By fuzzy simulation,the fuzzy linguistic variables are transformed as the weights of web pages.In addition,it is inaccurate that only visiting frequency discloses the user interest.Therefore,based on the concept of weighted support and preference,mining user preferred browsing patterns can be done from FLaAT(Frequent Link and Access Tree),which stores all user browsing information.The algorithm is effective by an experiment.