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当今煤炭业已经进入了信息时代,基于粗糙集的非完备信息系统决策规则学习算法,能提高属性未知数据信息的准确表达。在该算法中,首先利用粗糙集理论对属性未知的数据信息进行属性约简,然后基于数据挖掘技术的关联规则发现方法和不确定决策方法进行属性未知数据信息的决策规则学习。通过实例数据集进行算法仿真分析,仿真表明,该算法具有较好的决策规则学习能力和表达准确性,验证了对属性未知数据信息利用经典粗糙集的上下近似等价关系进行约简和学习是有效的。
Nowadays, the coal industry has entered the information age, and the rules-based learning algorithm based on rough sets of incomplete information systems can improve the accurate representation of the unknown data. In the algorithm, firstly, attribute reduction is made to the data information whose attribute is unknown by using rough set theory, and then the association rule discovery method and the uncertain decision method based on data mining technology are used to study the decision rules of attribute unknown data information. The simulation results show that this algorithm has better learning ability and accuracy of decision rules, and it is proved that the reduction and learning of attribute unknown data information using upper and lower approximation equivalence relations of classical rough sets Effective.