Attribute reduction is a critical issue in rough sets theory. In recent years, there are many kinds of attribute reduction proposed, such as positive region preservation reduction, generalized decision preservation reduction, distribution preservation reduction, maximum distribution preservation reduction, and relative discernibility relation preservation reduction. General reduction approaches to obtaining various types of reducts also have been explored, but they are computationally time-consuming in the condition of large-scale data processing. In this study, we focus on the efficient general reduction algorithm to obtain five typical reducts mentioned above. At first, we introduce a concept called granularity space to establish a unified representation of five typical reducts. Based on the unified representation, we construct two quick general reduction algorithms by extending the positive region approximation to the granularity space. Then, we conduct a series of comparisons with existing reduction algorithms in aspects of theoretical analysis and experiments to evaluate the performance of the proposed algorithms. The results of analysis and experiments indicate that the proposed algorithms are effective and efficient.

属性约简是粗糙集理论中的关键问题。近年来，提出了多种属性约简，如正区域保留约简，广义决策保留约简，分布保留约简，最大分布保留约简和相对区分关系保留约简。还已经探索了获得各种类型归约的通用归约方法，但是在大规模数据处理的情况下，它们在计算上很耗时。在这项研究中，我们专注于有效的一般归约算法以获得上述五个典型的归约。首先，我们引入一个称为粒度空间的概念来建立五个典型归约的统一表示。基于统一表示，通过将正区域近似扩展到粒度空间，我们构造了两种快速的一般归约算法。然后，我们在理论分析和实验方面与现有的归约算法进行了一系列比较，以评估所提出算法的性能。分析和实验结果表明，该算法是有效的。