Web日志数据中的有效区间和频繁模式发现
Significant Interval and Frequent Pattern Discovery in Web Log Data
摘要：
Web日志数据的序列挖掘已经有相当多的研究工作。我们使用的是一次通过频繁事件发现（或FED）算法，采取了不同于传统的先验类模式检测算法的方法。在这种方法中，首先（独立地）计算每个网站的显著间隔，并且这些间隔用于检测频繁模式/事件，然后对显著间隔和频繁模式执行分析，其可以用于使用先前的趋势来预测用户的行为，并且这也可以用于广告目的。这种类型的应用程序预测网站的兴趣。在这种方法中，时间序列数据被折叠在一个周期（天，周等）。哪些是用来构成区间的？从满足用户指定的最小置信度和最大间期长度标准的这些时间点发现显著间期。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
