基于双链树的Web日志挖掘
Efficient Web Log Mining using Doubly Linked Tree
摘要：
万维网是一个巨大的数据库，并以每天约100万页的爆炸性速度增长。随着万维网上可用信息的增长，网站的使用也在增长。Web日志记录了用户对Web页面的每次访问，并且Web日志中的条目数量正在迅速增加。这些网络日志，如果挖掘得当，可以为决策提供有用的信息。网站的设计者、分析师和管理人员都有兴趣从网络日志中提取这些隐藏的信息，以供决策之用。Web访问模式是从Web日志中挖掘出的重要信息之一，它是Web用户频繁使用的访问序列。这些信息可用于收集商业智能，以提高销售和广告，为用户提供个性化服务，分析系统性能并改进网站组织。有许多技术可以从Web日志中挖掘访问模式。本文介绍了一种有效挖掘Web日志的算法。该算法首先将Web访问数据转换到一个特殊的双向链接树中。每个访问被称为一个事件。此树基于频繁事件计数以非常压缩的形式保存关键的挖掘相关信息。建议的递归算法使用此树有效地找到所有的访问模式，满足用户指定的标准。为了证明我们的算法是有效的，从其他的GSP（广义序列模式）算法，我们做了实验研究的样本数据。
英文摘要：
World Wide Web is a huge data repository and is growing with the explosive rate of about 1 million pages a day. As the information available on World Wide Web is growing the usage of the web sites is also growing. Web log records each access of the web page and number of entries in the web logs is increasing rapidly. These web logs, when mined properly can provide useful information for decision-making. The designer of the web site, analyst and management executives are interested in extracting this hidden information from web logs for decision making. Web access pattern, which is the frequently used sequence of accesses, is one of the important information that can be mined from the web logs. This information can be used to gather business intelligence to improve sales and advertisement, personalization for a user, to analyze system performance and to improve the web site organization. There exist many techniques to mine access patterns from the web logs. This paper describes the powerful algorithm that mines the web logs efficiently. Proposed algorithm firstly converts the web access data available in a special doubly linked tree. Each access is called an event. This tree keeps the critical mining related information in very compressed form based on the frequent event count. Proposed recursive algorithm uses this tree to efficiently find all access patterns that satisfy user specified criteria. To prove that our algorithm is efficient from the other GSP (Generalized Sequential Pattern) algorithms we have done experimental studies on sample data.
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。
