【摘要】 为发现W eb使用记录中潜在的用户访问行为,提出了一种基于层次关联规则的日志本体事件领域关系学习方法。该方法利用日志本体中复合事件与原子事件之间的整分关系确定事务粒度,将关联规则挖掘算法扩展到事件层次结构上以发现候选频繁用户使用规则,在此基础上修剪冗余和无效的规则后抽取出事件间潜在的领域关系,达到丰富日志本体的目的。最后进行仿真实验,实验结果表明了该方法的可行性和有效性。更多还原

【Abstract】 In order to discover the potential user-access patterns in Web usage records,this paper presented an approach for domain relation learning from events in log ontologies based on hierarchy association rules.This method fixed on the granularity of transactions through the part-whole relation between complex events and atom events in log ontologies,and extended the association rules mining algorithm on the hierarchy of events to discover the candidate frequent usage rules.Domain relations of events could be extracted after refining the redundant and noneffective rules.The experimental results show that this method can enrich the log ontologies and it is quite feasible and effective to solve practical problems.更多还原