Parallel reduction based on condition attributes

In this paper, we propose a parallel algorithm to obtain reducts. The algorithm firstly divides the decision system into a number of subsystems, then reduce conditional attributes in each subsystem, and merge some subsystems together. This process repeats until all subsystems are merged into one. In this way, conditional attributes are reduced efficiently. The effectiveness of our method is analyzed and validated through experiments on seven datasets.