Mining sequential association-rule for improving WEB document prediction

Currently, researchers have proposed several sequential association-rule models for predicting the next HTTP request. These researches focus on using sequence and temporal constrains for pruning to improve prediction precision. In this paper, we provide a comparative study on different kinds of sequential association rules for web document prediction. Firstly, we give algorithms on mining sequential association rules, which is based on different sequence and temporal constrains combination. Then, the performance of all such algorithms has been compared on a real web log dataset. Based on the comparison, by the method of variance analysis, we explore the effect of sequence and temporal information on influencing the precision of prediction. We show that the sequence constrains, the temporal constrains and the interaction between these two constrains can affect the precision of prediction. Furthermore, temporal constrains can affect more than sequence constrains. These results show light on the future research on improving the precisions of prediction.