混合信息系统的动态变精度粗糙集模型

粗糙集是一种针对不确定性数据的数据挖掘理论,邻域粗糙集是处理混合型数据的常用模型.为了提高对混合型数据的抗噪能力,提出一种混合信息系统的变精度粗糙集模型;由于现实环境下信息系统的动态性,进一步提出对象增加和减少时的动态变精度粗糙集模型.首先研究混合信息系统中条件概率随对象增加和减少时的变化关系,然后在该变化关系的基础上提出混合信息系统变精度粗糙集上下近似的增量式更新机制,最后根据这一更新机制提出相应的增量式近似更新算法.实验结果表明,所提出的增量式更新算法比非增量的算法具有更高的计算效率,从而验证了所提出模型的有效性,同时也表明所提出模型更加适用于复杂的数据环境.

Dynamic variable precision rough set model of mixed information system

Rough set is a data mining theory for uncertainty data, and neighborhood rough sets are common models for dealing with mixed data. In order to improve the anti noise ability of mixed data, a variable precision rough set model of the mixed information system is proposed. Due to the dynamics of the information system in the real environment, this paper further proposes a dynamic variable precision rough set model when the object is increased and reduced. Firstly, the changed relation of conditional probability with the increasing and decreasing object in the mixed information system is studied. Then, on the basis of this changed relation, an incremental updating mechanism of the upper and lower approximation of the variable precision rough set of the mixed information system is proposed. Finally, according to this updating mechanism, the corresponding incremental approximation updating algorithm is proposed. Experimental results show that the proposed incremental updating algorithm has higher computational efficiency than the non-incremental algorithm, thus the effectiveness of the proposed model is validated, meanwhile, the proposed model is more suitable for complex data environments.