基于频繁的Markov链预测模型  

Markov Chain Model of Navigation Based on Frequence

作　　者：闫永权[1] 张大方[1] 

机构地区：[1]湖南大学软件学院,湖南长沙410082

出　　处：《计算机应用研究》2007年第3期41-43,46,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60473031)

摘　　要：预取技术通过在用户浏览当前网页的时间内提前取回其将来最有可能请求的网页来减少实际感知的获取网页的时间。传统的Markov链模型是一种简单而有效的预测模型,但同时存在预测准确率偏低,存储复杂度偏高等缺点。通过提出一种算法来减小存储空间,最后通过证明能有效减小存储空间。Prefetching can reduce the retrieval time perceived by users by predicting and fetching the most likely web pages that are to be requested soon, while the user is browsing through the current displayed page. Markov chain is a simple and practical model, but it gives a little low prediction accuracy and requires a little high space complexity. An algorithm to reduce space was presented, finally, it demonstrated this algorithm can reduce space.

关 键 词：预取 马尔可夫模型 频繁模式树 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]