混合数据的邻域区分度增量式属性约简算法

增量式属性约简是一种针对动态环境下的数据挖掘方法.目前已经提出的增量式属性约简算法仅适用于符号型的信息系统，而很少有对混合信息系统进行相关的研究，这促使在混合信息系统下构建相关的增量式属性约简算法.区分度是用于设计属性约简的一种重要方法，本文将传统的区分度在混合信息系统下进行推广，提出邻域区分度的概念，然后分别研究了邻域区分度在混合信息系统下对象增加和对象减少时的增量式学习，最后根据这种增量式学习分别提出了对应的增量式属性约简算法.UCI数据集上的相关实验结果表明，所提出的增量式属性约简比非增量式属性约简能够更快速的更新约简结果.

Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data

Incremental attribute reduction is a data mining method for dynamic environment.The incremental attribute reduction algorithm already proposed is only applicable to symbolic information systems.However,there are few related studies on mixed information systems,which promotes the construction of the related incremental attribute reduction algorithm under the mixed information system.The discernibility degree is an important method used for designing attribute reduction.In this paper,the traditional discernibility degree is generalized under the mixed information system,and the concept of neighborhood discernibility degree is presented.Then,the incremental learning of neighborhood discernibility degree is studied respectively when objects increase or objects decrease under the mixed information system.Finally,according to this incremental learning,the corresponding incremental attribute reduction algorithms are proposed,respectively.The related experimental results on the UCI data set show that the proposed incremental attribute reduction can update the reduction results more quickly than the non incremental attribute reduction.