A Markov prediction model based on page hierarchical clustering

被引:0
作者
Yao, Yao [1 ,2 ]
Shi, Lei [1 ,2 ]
Wang, Zhanhong [3 ]
机构
[1] Henan Prov Key Lab Informat Network, Zhengzhou 450052, Henan, Peoples R China
[2] Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Henan, Peoples R China
[3] Xinyang Normal Univ, Dept Comp Sci, Xinyang 46400, Peoples R China
来源
INTERNATIONAL SYMPOSIUM ON ADVANCES IN COMPUTER AND SENSOR NETWORKS AND SYSTEMS, PROCEEDINGS: IN CELEBRATION OF 60TH BIRTHDAY OF PROF. S. SITHARAMA IYENGAR FOR HIS CONTRIBUTIONS TO THE SCIENCE OF COMPUTING | 2008年
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中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Markov prediction model is the basis of Web prefetching and personalized recommendation. It can be used to extract connotative Web link hierarchy. But the existence of a large amount of Web objects results in data redundancy and model hugeness. This paper presents an improved method that simplifies the topology structure of Web site and extracted the conceptual link hierarchy which can make the organization clearly and legibly. Firstly, Markov Tree is built. Secondly web site link hierarchy is obtained. Then the in-link and out-link similarity are used to find similar pages in the same level which can be clustered together in order to reduce the dimensionality of the transition matrix. Finally, apply above model to predict. Thus the model can help users find information more effectively and efficiently. Experiments based on two real Web log data demonstrate the efficiency of the proposed method, which can not only have good overall performance and clustering effect but also keep relative higher prediction accuracy and recall.
引用
收藏
页码:499 / 504
页数:6
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