基于序列关联规则挖掘的Web日志预测精度研究

Mining Sequential Association Rule for Improving Web Document Prediction

目前许多研究关注如何利用序列关联规则预测用户最近的HTTP请求,这些研究主要利用次序信息或时间信息来进行剪枝,以提高预测的精度。该文对不同序列关联规则进行了分析和比较,给出了不同次序信息和时间信息的条件下各种序列模式挖掘算法。并使用实验比较这些算法的预测精度。通过对实验结果的分析,为进一步提高预测的精度指明了方向。

Currently, researchers have proposed several sequential association rule modes for predicting the next HTTP request. These researches focus on using sequence and temporal constrains for pruning to improve prediction precision. This paper provides a comparative study on different kinds of sequential association rules for Web document prediction, gives algorithms on mining sequential association rules, which is based on sequence and temporal different combination. The performance of all such algorithms has been compared on a real Web log dataset. Based on the comparison, using analysis of variance method, the effect of sequence and temporal information on influencing the precision of prediction is explored.