文中提出一种离散和连续混合属性的复杂信息系统增量式属性约简算法． 首先， 将粒计算模型中的知识粒度在混合型信息系统下进行推广，提出了邻域知识粒度，并构造 出基于邻域知识粒度的非增量式属性约简算法，然后在混合型信息系统下研究了邻域知 识粒度随对象增加时的增量式计算，理论证明了该计算方式的高效性，最后提出了基于邻 域知识粒度的混合信息系统增量式属性约简算法． UCI 数据集的实验结果表明，所提出的 算法在混合型信息系统中具有很高的增量式属性约简性能．

An incremental attribute reduction algorithm for complex information systems with discrete and continu- ous mixed attributes was proposed． Firstly，the knowledge granulation in the granular computing model was extend- ed under the mixed information system，the neighborhood knowledge granulation was proposed，and a non-incre- mental attribute reduction algorithm based on the neighborhood knowledge granulation was constructed． Then，the incremental computation of neighborhood knowledge granulation with the increase of objects was studied under the mixed information system，and the efficiency of the computation was proved theoretically． Finally，an incremental attribute reduction algorithm for mixed information systems based on neighborhood knowledge granulation was pro- posed． Experimental results of UCI datasets show that the proposed algorithm has high incremental attribute reduc- tion performance in mixed information systems．