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Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05)
Mining Sequential Association-Rule for Improving WEB Document Prediction
Las Vegas, Nevada
August 16-August 18
ISBN: 0-7695-2358-7
Yong Wang, Northwestern Polytechnical University
Zhanhuai Li, Northwestern Polytechnical University
Yang Zhang, Northwestern Polytechnical University
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.
Index Terms:
Sequential Association Rule, Web Usage Mining, Analysis of Variance
Citation:
Yong Wang, Zhanhuai Li, Yang Zhang, "Mining Sequential Association-Rule for Improving WEB Document Prediction," iccima, pp.146-151, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005
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