面向对象变化的局部邻域粗糙集增量更新算法

Incremental updating algorithm of local neighborhood rough sets with the variation of objects

局部邻域粗糙集降低了近似集计算的时间复杂度,为数值型数据的知识发现提供了一种高效的方法.现实环境下的数据通常是数值型并且会随着时间的变化而动态变化.为了对动态数值型数据进行有效的知识发现,提出一种局部邻域粗糙集模型的增量更新算法.分析对象集变化时局部近似集的更新公式,设计动态数值型数据的局部近似集的更新算法.实验结果表明,所提出的算法能够有效利用已有知识,比非增量算法具有更高的计算效率.

The local neighborhood rough set reduces the time complexity to calculate the approximation set and provides an efficient method for the knowledge discovery of numerical data.The data in real environment is usually numerical and will change dynamically with time.In order to perform effective knowledge discovery on dynamic numerical data,an incremental updating algorithm of local neighborhood rough set model is proposed in this paper.In this paper,we analyze the updating formula of local approximation set when the object set changes,and designs the updating algorithm of local approximation set of dynamic numerical data.The experimental results show that the proposed algorithm can effectively utilize the existing knowledge and has higher computational efficiency than non-incremental algorithm.