基于粗糙集理论的数据挖掘模型

提出了一种基于粗糙集理论的数据挖掘模型，以利于信息不完备情况下的推理和决策问题的解决和研究。该模型从已知数据的初始决策系统出发，建立一系列的不同简化层次的子系统，然后推导出各个子系统的规则集，其中每条规则都有相应的置信度。在应用模型进行推理和决策分析时，用给定对象的信息与模型中相应节点的规则进行匹配，然后选用某种评判算法得出结论。给出了一个简单的例子来说明如何建立和应用这种数据挖掘模型。这样的模型可以很方便地根据给定的信息，在最符合的子系统上得出尽可能好的结论。

Data mining model based on rough set theory

On purpose of improving the solution and research in reasoning and decision making problems when the information in hand is not sufficient, a data mining model based on rough set theory is presented. From the initial decision system defined by the primitive data, a series of sub systems with various reductive levels are created. For each sub system, the rule sets with respective belief degrees are induced and saved. When applying the model to reasoning and decision analysis, one can match the information of the given object to the rule sets of relative nodes, and then draw the conclusion by using some kind of evaluation algorithm. A simple example on how to create and apply the model is given. The presented model can be applied conveniently by selecting suitable sub systems in accordance with the given information and computing the best decision from the rules in those sub systems.