Incremental attribute reduction algorithm based on neighborhood granulation conditional entropy

Incremental attribute reduction is an important data mining method for dynamic data. The incremental attribute reduction algorithms proposed at present are mostly based on discrete data construction, but the related study for numeric data is few. Therefore, an incremental attribute reduction algorithm for object constantly increasing in numeric information system is presented. Firstly, a hierarchical neighborhood computing method is established in numeric information system, and the incremental computing of neighborhood granulation based on this method is proposed. Then, on the basis of neighborhood granulation incremental computing, the incremental updating method of neighborhood granulation conditional entropy is given, and the corresponding incremental attribute reduction algorithm is proposed on account of this updating mechanism. Finally, experimental analysis shows that the proposed algorithm has higher effectiveness and superiority for the incremental attribute reduction of numerical data. © 2019, Editorial Office of Control and Decision. All right reserved.