Web日志挖掘中的会话识别算法

Improved method for session identification in web log mining

会话识别是Web日志挖掘的关键步骤,然而很多方法所得到的会话不够精确。针对Web日志挖掘中的会话识别问题,在最常用的Timeout方法的基础上,提出了一种改进的基于平均时间阈值的识别方法。通过动态计算会话中请求记录间的平均时间间隔,个性化地调整页面的时间阈值,相对于传统的对所有用户页面使用单一的先验阈值,该方法能够更准确地识别出长对话。最后对生成的侯选会话集进行二次识别,使识别出的会话更为合理有效。实验结果表明,会话质量得到了提高。

Although session identification is an important step in web log mining, the session identified by existing methods are not accurate. Toward session identification in web log mining, an access timeout-based improvement is carried out of session identification in web log mining. By calculating the average intervals dynamically among request records in the session, adjusting a threshold individually. Compared to the traditional method that defines a uniform threshold for all web pages experimentally, the approach presents can identify the long session more accurately. Then generating sets of candidate session is re-identified, which make the session more reasonable and effective. The quality of session identification is proved more efficiency by experiments.