粗糙集中一种连续属性预处理方法

针对在数据挖掘中，连续属性常常需要预处理问题，应用粗糙集理论对连续属性的不完备问题、离散问题进行了研究，提出了一种连续属性预处理方法。基于条件属性与决策属性间的对应关系完成了不完备数据的填补。依据划分区间的概念、连续属性离散化含义及其本质特征，定义了划分区间的加法运算法则，以此对填补后的信息表进行了划分区间运算，并以分类质量作为离散过程迭代约束条件，实现了信息表中连续属性的离散化。通过C 编写的算法进行数值示例及测试数据库。实验结果表明此算法有效可行。

An algorithm for preprocessing continuous attributes in rough sets

In data mining, continuous attributes sometimes need to be preprocessed. Based on rough set, the incomplete problem and the discretization problem are studied. And meanwhile a new algorithm for preprocessing continuous attributes is proposed. The incomplete data were filled up depending on the correlation between condition and decision attributes. According to the concept of demarcation and its essential, the paper defines a plus rule for the interval values. After adding interval values to each attribute with iterative constraints of classification quality, the continuous attributes were discretized. The illustration and experiment were done by the C++ program and the results indicate that the method is effective for preprocessing continuous attributes.