快速的属性约简算法

属性约简的效率是粗糙集等软计算理论的核心问题之一。为了提高约简效率，在分析不可分辨关系和基数排序特点的基础上，提出了一种时间复杂度为O（|C||U|）的求核算法。然后，运用改进的属性重要度作为启发信息，得到一种快速的属性约简算法，时间复杂度为O（|C|²|U|）。最后，通过UCI机器学习库中的一些数据集对算法进行测试，证明了算法对大型的数据集进行属性约简的高效性。

New algorithm for attribute reduction

The efficiency of attribute reduction is a key issue in rough set and other soft computing theories.In order to enhance it，a new algorithm for computing core is proposed after analyzing the indiscernibility relation and radix sorting，and its time complexity is O（|C||U|）.Furthermore，a quick reduction algorithm which uses improved attribute significance as heuristic information is presented，the time complexity is O（|C|²|U|）.Finally，through some experiments on the data sets in UCI machine learning repository，the algorithm is proved more efficient and suitable for large data sets.