一种基于页面价值和跳转偏爱度挖掘频繁访问路径的模型

A Novel Model for Mining Frequent Paths Based on Page Value and Jump Preference Degree

设计实现了一种从Web日志挖掘用户频繁访问路径的模型.提出网页聚类分析的一个重要基础理论,以及页面价值和跳转偏爱度的概念,并建立页面价值模型.该模型从页面价值-用户矩阵计算出页面价值间的加权欧氏距离,并由距离大小获得等价值页面集.再根据跳转偏爱度把等价值页面集转化为2-项频繁访问子路径集,并经过自适应的合并算法得到最终的频繁访问路径集.实验证明该页面价值模型能高效获得更精准的频繁访问路径.

A model is designed and implemented for mining user frequent paths from Web log. An important basis for webpage clustering analysis is proposed, as well as the concepts of page value and jump preference degree. Then we build the page value model. The distances of page value are calculated out from the Page Value-User Matrix, then the value-equal page set is obtained according to the distances. After that, the set is transformed to binomial frequent path set. Finally the user frequent path set can be obtained by applying an adaptive merging algorithm. Experimental results show the model has better accuracy with high efficiency.