An incremental attribute reduction method for dynamic data mining.

As an important preprocessing step for data mining, attribute reduction has become a hot research topic in rough set theory. In practice, many real data may vary dynamically with time, therefore, reduct will change dynamically under the variation of objects and attributes in decision systems. The classical attribute reduction methods need to recompute from scratch, which are ineffective to deal with dynamic decision systems. How to implement updating reducts by utilizing previous results is vital for improving the efficiency of attribute reduction approaches. In the paper, we firstly introduce incremental mechanisms of computing reduct when objects and attributes of the decision system change dynamically. Then, incremental methods are developed to update reduct when attributes and objects increase simultaneously. Finally, a series of experiments are conducted to validate the proposed incremental attribute reduction methods. Experimental results show that they are effective to update reduct with change of attributes and objects in the decision systems.