| Publisher | University of Waterloo | ||
|---|---|---|---|
| Format | 763.5KB PDF, requires Acrobat Rdr 5 | Date added | 01 Sep 2004 |
| Topics | Web Reporting | ||
| Downloads | 48 | ||
Due to the rapid expansion of the World Wide Web, the use of automated web mining techniques to discover useful, relevant information has become an increasingly important research area. One important sub-area is web usage mining, wherein one attempts to discover patterns of web usage from web log data. Although web log data is usually noisy and extremely ambiguous, there remains a potential for discovering useful structure in the interactions between a web site and its users. This paper presents a novel session identification method based on statistical language modeling. The basic goal of session identification is to group sequential log entries that are related to a common topic, and segment log entries that are unrelated.
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