Web-log Mining for Quantitative Temporal-Event Prediction

The web log data embed much of web users' browsing behavior. From the web logs, one can discover patterns that predict the users' future requests based on their current behavior. These web data are very complex due to their large size and sequential nature. In the past, researchers have proposed different methods to predict what pages will be visited next based on their present visit patterns. In this paper, we extend this work to discover patterns that can predict when these web page accesses will occur. Our method is based on a novel extension of association rule classification method. We extend the traditional association rules by including the temporal information explicitly in each rule, and reason about the confidence of each prediction in terms of its temporal region. We compare two different methods for temporal event prediction, demonstrate the effectiveness of our methods empirically on realistic web logs, and explore the tradeoff between prediction accuracy and dat...

• Type: Journal
• Year: 2002
• Where: CIB
• Authors: Qiang Yang, Hui Wang, Wei Zhang
• Keywords: Association Rules, CIB 2002, Present Visit Patterns, Web Log Data