【摘要】 文章介绍一种基于推广分布表(GDT)和粗糙集的从不确定、不完整数据库中挖掘if-then规则的新方法.GDT是描述离散范畴的概念和实例的概率关系的表,通过使用GDT作为设定的搜索空间,将粗糙集与GDT相结合,可以处理噪声和未知实例.强度较大的if-then规则可以有效地按自底向上逐步增加的方式从大量的、复杂的数据库中获得.更多还原

【Abstract】 There introduces a new approach for mining if-then rules in databases with uncertainty and incompleteness.The approach is based on the combination of Generalization Distribution Table(GDT) and the Rough Set methodology.A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented.By using a GDT as a hypothesis search space and combining the GDT with the rough set methodology,noises and unseen instances can be handled,and if-then rules with strengths can be effectively acquired from large,complex databases in an incremental,bottom-up mode.更多还原