基于经典粗糙集的近似集动态获取方法

摘要

The data in information system is dynamically changed .How to acquisite useful information ac‐cording to dynamical varied information system is a key problem in data processing .To deal with the problem ,the approaches for dynamical approximations acquisition w hile adding or deleting an attribute are respectively discussed in information system .By dividing original equivalent classes in information systems ,an approach which avoids re‐division of the universe is proposed .The efficiency of dynamical updating approximation is improved .By analyzing the relationship between equivalent classes and original approximations ,the corresponding theorems between updated approximations and original approxima‐tions are given .Then ,the approaches for dynamical acquisition of approximations while adding or dele‐ting an attribute are respectively proposed in classical rough set model .Experimental results verify the validity of the approaches and prove that the efficiencies of the proposed approaches are better than those of the original approaches .%信息系统中的数据是动态变化的，根据动态变化的信息系统获取有用的信息，成为数据处理中的关键问题。针对该问题，分别讨论了信息系统中属性增加和减少时，近似集的动态获取方法。通过对信息系统中原有的等价类进行划分，避免了对论域的重新划分，提高了动态更新近似集的效率，通过讨论等价类与原有近似集之间的关系，给出了信息系统动获取之后的近似集与原来近似集之间的相关定理，提出了在经典粗糙集模型中，属性增减时近似集动态获取方法。实验结果验证了该方法的正确性和有效性，而且效率优于原始的方法。