文章基本信息

标题：Significant Interval and Frequent Pattern Discovery in Web Log Data
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作者：Kanak Saxena ; Rahul Shukla
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2010
卷号：7
期号：1
页码：29-36
出版社：IJCSI Press
摘要：There is a considerable body of work on sequence mining of Web Log Data We are using One Pass frequent Episode discovery (or FED) algorithm, takes a different approach than the traditional apriori class of pattern detection algorithms. In this approach significant intervals for each Website are computed first (independently) and these interval used for detecting frequent patterns/Episode and then the Analysis is performed on Significant Intervals and frequent patterns That can be used to forecast the user's behavior using previous trends and this can be also used for advertising purpose. This type of applications predicts the Website interest. In this approach, time-series data are folded over a periodicity (day, week, etc.) Which are used to form the Interval? Significant intervals are discovered from these time points that satisfy the criteria of minimum confidence and maximum interval length specified by the user.
关键词：Web log data; minimum confidence; periodicity; significant interval discovery; Frequent Episode/Pattern; Web access; access count