一种基于粗糙集的决策算法挖掘方法

属性约简和属性值约简是利用粗糙集理论从决策表中挖掘决策规则的基础,挖掘决策规则是粗糙集理论的重要研究领域之一。本文根据属性的重要度和条件差别矩阵来进行条件属性约简,找出有效约简。同时定义了决策表的不一致度,并根据属性重要度来进行属性值约简,在保持决策表不一致度不增加的前提下挖掘出决策规则,该决策规则集满足独立性、覆盖全域性、可接受性和一致性,即决策规则集为决策算法,最后运用实例对该方法的有效性进行说明。

A Method of Rough Set Based Decision Algorithm Mining

Reduction of attributes and reduction of attributes' value are the base of mining decision rules from decision table using rough set theory, and mining decision rules is one of the most important research fields in rough set theory. Based on the important degree of attributes and conditional discernibility matrix, an effective conditional attributes reduction is obtained. A notation of inconsistency of decision table is given, the attributes' value reductions are got by the important degree of attributes and decision rules is obtained in the premise of maintaining the inconsistency of the decision table. Using this method, the set of decision rules which are mined from decision table satisfies the properties of independent, covering universe, admissible and consistency, that is, it is a decision algorithm in decision table, finally, the correctness and effectiveness of the method are shown by an example.