Parallel reducts for incremental data

Parallel reducts are more suitable for dynamic and incremental data than other reducts, and can be obtained by attribute significance in a family of decision subsystems. However, when data are increasing, they should be improved or changed to fit the new data set. In this paper, some properties of parallel reducts for changing data are discussed, and an algorithm for improving parallel reducts is proposed. Experimental results show that the algorithm can reduce most of time for calculating new parallel reduct when new data are increasing.