【摘要】 事务数据库中频繁模式的挖掘研究作为关联规则等许多数据挖掘问题的核心工作,已经研完了许多年。然而,频繁模式挖掘算法经常产生大量的模式和规则,不但降低了算法的执行效率,同时也使用户从频繁模式产生有用的规则变得很困难。针对这个问题,最近的研究主要集中于两点,一种方法是允许用户附加约束来引导挖掘的过程,通过把约束条件下推到挖掘的底层来缩小模式搜索的空间,提高性能;另一种方法是仅挖掘闭合模式,只产生大于其超集支持度的频繁模式。两种方式都可以大量缩小结果集合的大小,使结果集合更容易被用户理解和使用。那么,把这两种方式相结合,挖掘满足用户约束的闭合频繁模式,理论上来说应该更为高效,更方便理解和使用。基于以上的考虑,做了一些细致的研究,把用户约束分类,并主要讨论了结合项约束的闭合模式生成问题。更多还原

【Abstract】 Mining frequent patterns in transaction databases, as an essential role in many data mining tasks such as the association rule mining ,has been widely studied for many years. However ,frequent pattern mining often generates a very large number of patterns and rules,which reduces not only the efficiency but also the effectiveness of mining. Recent work focuses mainly on two aspects. One is to mine frequent closed patterns; the other is to mine frequent patterns with user’s constraints. These two points can both reduce the number of result patterns; make the users easier to understand. Therefore theoretical, if we combine these two points to mine frequent closed patterns with user’s constraints, the result set should be more easier to understand. Based on the above, we make some careful research, classify the constraints, and mainly focus on the problem of mining frequent closed patterns with item constraints.更多还原